Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to predictive healthcare systems, more specifically, relates to an intelligent health monitoring system for early diabetes prediction and proactive health management based on multi-source data integration, real-time physiological monitoring, and machine learning-based analysis.
BACKGROUND OF THE DISCLOSURE
[0002] Predictive healthcare systems utilize advanced data analysis, including statistical modelling, data mining, artificial intelligence (AI), and machine learning to analyse large volumes of health-related data to forecast future health outcomes and trends, enabling proactive interventions and improved patient care. By detecting health risks before symptoms manifest, predictive healthcare systems enable timely interventions, personalized treatment plans, and improved patient outcomes, while also reducing the burden on healthcare infrastructure.
[0003] Diabetes is a chronic metabolic disorder that affects millions of individuals worldwide and often remains undiagnosed until advanced stages. Early detection and continuous management of diabetes are critical to reducing the risk of serious complications such as cardiovascular disease, kidney failure, and nerve damage. Conventional methods of diabetes screening rely heavily on periodic medical tests and self-reporting, which can be inconvenient, invasive, reactive in nature, and insufficiently personalized.
[0004] Current diabetes prediction systems often focus on isolated data sources and lack real-time feedback mechanisms or holistic lifestyle assessments. Conventional systems are typically limited in scope, relying heavily on static data such as genetic markers or periodic health check-up results. These approaches often neglect dynamic, real-time data sources such as physical activity, dietary intake, stress, and sleep patterns, which are critical in assessing an individual’s evolving health status. As a result, they fail to offer a complete understanding of a user’s risk profile, reducing the accuracy and relevance of predictions.
[0005] Furthermore, existing prediction systems generally lack personalization. Many systems do not consider individual behavioural patterns or daily habits, such as specific dietary choices, hydration, mental health indicators, or irregular sleep schedules. These lifestyle factors significantly influence the development and progression of diabetes. Without analysing user-specific data such systems cannot generate tailored recommendations that adapt to each user's unique health context.
[0006] Additionally, most conventional systems operate on a reactive basis, detecting elevated glucose levels or complications only after significant disease progression. They do not offer continuous monitoring or generate predictive alerts to prompt early medical consultation. The absence of real-time feedback mechanisms and preventive interventions limits their effectiveness in long-term diabetes management. Consequently, current systems do not empower individuals to take proactive steps toward preventing or managing diabetes in its early stages. They are often reactive, providing insights only after symptoms develop or during infrequent medical check-ups.
[0007] Furthermore, many existing diabetes prediction systems do not adequately address the privacy and security of sensitive health data. Without strong data protection mechanisms, including permission-based sharing and secure storage, users face potential risks regarding unauthorized access or misuse of their personal health information undermining trust and adoption of such digital health solutions.
[0008] The present invention overcomes the disadvantages of the prior art by providing an intelligent health monitoring system for early diabetes prediction and proactive health management. The present invention adopts an integrated approach by combining genetic reports, historical medical records, real-time physiological data from wearable devices, and AI-driven analysis of dietary and lifestyle patterns derived from user-generated text and images. The present invention leverages advanced machine learning algorithms to deliver continuous risk assessment, generate predictive alerts, and provide real-time, personalized coaching through an AI-powered virtual assistant. Unlike existing systems that consider isolated health factors, the system of the present invention performs holistic profiling using advanced text and image processing. Furthermore, the use of blockchain technology ensures secure, private, and permission-based sharing of sensitive health data. The present invention bridges the gap between fragmented health tracking and comprehensive, preventive diabetes care.
[0009] Thus, in light of the above-stated discussion, there exists a need for an intelligent health monitoring system for early diabetes prediction and proactive health management.
SUMMARY OF THE DISCLOSURE
[0010] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0011] According to illustrative embodiments, the present disclosure focuses on an intelligent health monitoring system for early diabetes prediction and proactive health management which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0012] The present invention solves all the above major limitations of an intelligent health monitoring system for early diabetes prediction and proactive health management.
[0013] An objective of the present disclosure is to provide an intelligent health monitoring system for early diabetes prediction and proactive health management.
[0014] Another objective of the present disclosure is to integrate multi-source health data including genetic reports, historical medical records, real-time physiological signals from wearable devices, and user-provided inputs such as text and images related to diet and lifestyle for accurate diabetes risk prediction.
[0015] Another objective of the present disclosure is to utilize machine learning algorithms for processing and analysing the integrated data to determine user-specific diabetes risk scores with high accuracy.
[0016] Another objective of the present disclosure is to provide continuous health monitoring and real-time tracking of physiological and behavioural data relevant to diabetes risk.
[0017] Another objective of the present disclosure is to generate predictive alerts upon detection of elevated diabetes risk levels or deteriorating health patterns, prompting timely user intervention or medical consultation.
[0018] Yet another objective of the present disclosure is to deliver personalized feedback and recommendations related to diet, physical activity, and lifestyle adjustments through an AI-based virtual assistant.
[0019] Yet another objective of the present disclosure is to ensure secure data handling by implementing a blockchain-based framework for encrypted data storage and permission-controlled sharing of sensitive health data.
[0020] In light of the above, in one aspect of the present disclosure, an intelligent health monitoring system for early diabetes prediction and proactive health management is disclosed herein. The system comprises a data acquisition unit configured to collect user-health related data from a plurality of sources. The system also includes a communication network configured to transmit data between the various components of the system. The system also includes a microprocessor connected to the data acquisition unit via the communication network and configured to process health related data for diabetes prediction, wherein the microprocessor further comprises a data input module configured to receive input data from the data acquisition unit, a data processing module configured to clean, normalize, and pre-process the received multi-format data to prepare the data for subsequent analysis, a feature extraction module configured to extract relevant features from the processed text and image data, a classification module configured to classify the user into predefined risk classes based on the extracted features, a diabetes prediction module configured to determine a user-specific diabetes risk score and predict the likelihood of developing diabetes based on the classified data utilizing machine learning models, a feedback module configured to provide real-time feedback and generate personalized recommendations for dietary intake, physical activity, and behavioural modifications based on the predicted diabetes risk score, an alert generation module configured to generate preventive health alerts upon detection of a risk score exceeding a predefined threshold value, and an output module configured to transmit processed insights, alerts, and recommendations to the user. The system also includes a user interface connected to the microprocessor via the communication network and configured to interact with the user to receive manual inputs, display risk levels, recommendations and alerts, and provide access to personalized health dashboards and reports.
[0021] In one embodiment, the data acquisition unit collects user-health related data from a plurality of sources, including but not limited to clinical genetic reports, historical medical records, real-time physiological data obtained from wearable sensors, nutritional related data obtained from user-captured meal images, and lifestyle-related data obtained from user-entered text inputs and user-captured images.
[0022] In one embodiment, the real-time physiological data obtained from wearable sensors comprises data, including but not limited to glucose levels, heart rate, body temperature, sleep patterns, blood oxygen levels, physical activity metrics, and stress indicators.
[0023] In one embodiment, the nutritional related data obtained from user-captured meal images comprises data, including but not limited to estimations of sugar, carbohydrate, fat, protein, and calorie content.
[0024] In one embodiment, the lifestyle-related data obtained from user-entered text inputs and user-captured images comprises data, including but not limited to hydration levels, dietary habits, physical activity, sleep patterns, stress indicators, and daily routine behaviours.
[0025] In one embodiment, the classification module classifies the user into predefined risk classes, including but not limited to low, moderate, and high diabetes risk, based on the extracted features.
[0026] In one embodiment, the microprocessor further comprises a data security and sharing module configured to manage secure storage and permission-controlled sharing of health data using a blockchain-based framework.
[0027] In one embodiment, the user interface further comprises a chatbot configured to provide personalized coaching and recommendations on diet, exercise, and health management based on the user's health data and risk assessment.
[0028] In one embodiment, the system further comprises a cloud database configured to store user-health related data, prediction results, and personalized recommendations for secure access, retrieval, and progressive health monitoring.
[0029] In light of the above, in another aspect of the present disclosure, a method for predicting diabetes and managing health proactively is disclosed herein. The method comprises collecting user-health related data from a plurality of sources via a data acquisition unit. The method also includes transmitting data between the various components of the system via a communication network. The method also includes processing health related data for diabetes prediction via a microprocessor comprising of several modules. The method also includes receiving input data from the data acquisition unit via a data input module. The method also includes cleaning, normalizing, and pre-processing the received multi-format data to prepare the data for subsequent analysis via a data processing module. The method also includes extracting relevant features from the processed text and image data via a feature extraction module. The method also includes classifying the user into predefined risk classes based on the extracted features via a classification module. The method also includes determining a user-specific diabetes risk score and predict the likelihood of developing diabetes based on the classified data utilizing machine learning models via a diabetes prediction module. The method also includes providing real-time feedback and generate personalized recommendations for dietary intake, physical activity, and behavioural modifications based on the predicted diabetes risk score via a feedback module. The method also includes generating preventive health alerts upon detection of a risk score exceeding a predefined threshold value via an alert generation module. The method also includes transmitting processed insights, alerts, and recommendations to the user via an output module. The method also includes interacting with the user to receive manual inputs, display risk levels, recommendations and alerts, and provide access to personalized health dashboards and reports via a user interface.
[0030] These and other advantages will be apparent from the present application of the embodiments described herein.
[0031] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0032] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0033] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0034] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0035] FIG. 1 illustrates a block diagram of an intelligent health monitoring system for early diabetes prediction and proactive health management, in accordance with an exemplary embodiment of the present disclosure; and
[0036] FIG. 2 illustrates a flowchart of a method, outlining the sequential steps for predicting diabetes and managing health proactively, in accordance with an exemplary embodiment of the present disclosure.
[0037] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0038] The intelligent health monitoring system for early diabetes prediction and proactive health management is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0039] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0040] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0041] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0042] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0043] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0044] Referring now to FIG. 1 to FIG. 2 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a block diagram of an intelligent health monitoring system 100 for early diabetes prediction and proactive health management, in accordance with an exemplary embodiment of the present disclosure.
[0045] The system 100 may include a data acquisition unit 102, a communication network 104, a microprocessor 108, and a user interface 110.
[0046] The data acquisition unit 102 in the system 100 is configured to collect user-health related data from a plurality of heterogeneous sources to enable comprehensive analysis and accurate prediction of diabetes risk.
[0047] In one embodiment of the present invention, the system 100 is implemented as a mobile and web-based application.
[0048] In one embodiment of the present invention, the data acquisition unit 102 collects user-health related data from a plurality of sources, including but not limited to clinical genetic reports, historical medical records, real-time physiological data obtained from wearable sensors, nutritional related data obtained from user-captured meal images, and lifestyle-related data obtained from user-entered text inputs and user-captured images.
[0049] In one embodiment of the present invention, the clinical genetic reports may include information on gene variants associated with diabetes predisposition, including but not limited to single nucleotide polymorphisms (SNPs), insertion-deletion mutations (indels), copy number variations (CNVs), structural variants, and point mutations.
[0050] In one embodiment of the present invention, historical medical records may include comprehensive health data relevant to the user’s medical history, including but not limited to blood test results, diagnostic reports, medication history, records of previous health conditions such as hypertension, obesity, or gestational diabetes, family medical history, clinical observations, and past treatment outcomes.
[0051] In one embodiment of the present invention, the real-time physiological data obtained from wearable sensors comprises data, including but not limited to glucose levels, heart rate, body temperature, sleep patterns, blood oxygen levels, physical activity metrics, and stress indicators.
[0052] In one embodiment of the present invention, the nutritional related data obtained from user-captured meal images comprises data, including but not limited to estimations of sugar, carbohydrate, fat, protein, and calorie content.
[0053] In one embodiment of the present invention, the lifestyle-related data obtained from user-entered text inputs and user-captured images comprises data, including but not limited to hydration levels, dietary habits, physical activity, sleep patterns, stress indicators, and daily routine behaviours.
[0054] In one embodiment of the present invention, the user may provide text-based inputs through the user interface 110 of the mobile or web-based application constituting the system 100.
[0055] In one embodiment of the present invention, the user may capture and upload image data using a camera integrated within the mobile or web-based application constituting the system 100.
[0056] The communication network 104 is configured to seamlessly transmit data between the various components of the system 100 for real-time diabetes prediction and proactive health management. The communication network 104 facilitates continuous and secure data exchange between the data acquisition unit 102, the microprocessor 108, and the user interface 110.
[0057] In one embodiment of the present invention, the communication network 104 may be both wired and wireless.
[0058] In one embodiment of the present invention, the communication network 104 may include, Wi-Fi, Bluetooth, Ethernet, cellular networks, LAN (Local Area Network), and VAN (Virtual Area Network).
[0059] The microprocessor 108 in the system 100 is operatively connected to the data acquisition unit 102 via the communication network 104 and is configured to process user-health related data collected from multiple sources for accurate diabetes prediction and proactive health management. The microprocessor 108 performs multi-stage data processing to facilitate intelligent decision-making based on real-time and historical health insights. The microprocessor 108 further comprises several modules including a data input module 112, a data processing module 114, a feature extraction module 116, a classification module 118, a diabetes prediction module 120, a feedback module 122, an alert generation module 124, and an output module 128.
[0060] The data input module 112 is configured to receive input data from the data acquisition unit 102. The data input module 112 serves as the initial interface for capturing raw, multi-format data from various sources to initiate further analysis and processing by the other modules of the microprocessor 108.
[0061] The data processing module 114 is configured to clean, normalize, and pre-process the received multi-format data to prepare the data for subsequent analysis. The data processing module 114 performs cleaning of the data by removing duplicates, handling missing values, and filtering out incorrect entries. The data processing module 114 also performs normalization to ensure data consistency across various sources, such as standardizing units and scaling values to a uniform range. Additionally, the data processing module 114 transforms unstructured data such as free-text inputs and images into structured formats that are compatible with downstream processing. The data processing module 114 aligns time-series data to ensure temporal synchronization of health signals collected from wearable sensors and other sources. The data processing module 114 also reduces noise and smoothens fluctuations in physiological signals to improve the reliability of extracted features. Furthermore, the data processing module 114 prepares the refined data in a format suitable for feature extraction, ensuring that the information is clean, consistent, and ready for accurate classification and prediction by subsequent modules of the microprocessor 108.
[0062] The feature extraction module 116 is configured to extract relevant features from the processed text and image data. The feature extraction module 116 is configured to extract semantic, statistical, and contextual features from user-provided text inputs and image data for use in subsequent classification and diabetes prediction.
[0063] In one embodiment of the present invention, the feature extraction module 116 may employ natural language processing (NLP) techniques to analyse user-entered text data. The relevant features extracted from text data may include, identifying key phrases, context-specific health indicators, frequency of behaviours, and semantic patterns related to diet, physical activity, sleep quality, and stress levels.
[0064] In one embodiment of the present invention, the feature extraction module 116 may employ computer vision and deep learning algorithms to identify food items, estimate portion sizes, determine nutritional values such as caloric and macronutrient content, analyse physical activity, and assess environment-related images to evaluate physical activity intensity and behaviour trends from user-provided images. These extracted features serve as structured inputs for subsequent classification by the microprocessor 108.
[0065] The classification module 118 is configured to classify the user into predefined risk classes based on the extracted features. The classification module 118 may utilize machine learning classification algorithms to analyse patterns and correlations within the extracted features, such as dietary habits, physical activity levels, genetic predispositions, and physiological signals. Based on this analysis, the user may be categorized into predefined risk classes.
[0066] In one embodiment of the present invention, the classification module 118 classifies the user into predefined risk classes, including but not limited to low, moderate, and high diabetes risk, based on the extracted features. Users classified into the low-risk category typically demonstrate healthy lifestyle behaviours such as balanced nutrition, regular physical activity, normal physiological parameters e.g., stable blood glucose and sleep patterns, and no significant genetic predisposition to diabetes. The moderate-risk category includes users exhibiting early risk indicators such as irregular dietary habits, inconsistent physical activity, occasional elevated physiological readings, or the presence of common gene variants associated with diabetes. Users classified into the high-risk category may show multiple risk factors, including strong family history, presence of high-risk genetic mutations, consistently unhealthy lifestyle patterns, abnormal medical parameters such as elevated fasting glucose or HbA1c levels, and stress-related indicators. This classification aids in personalized prediction of diabetes and targeted intervention strategies.
[0067] The diabetes prediction module 120 is configured to determine a user-specific diabetes risk score and predict the likelihood of developing diabetes based on the classified data utilizing machine learning models. The diabetes prediction module 120 analyses the user’s classified risk category along with extracted health indicators to compute a categorical risk score. This risk score reflects the probability of the user developing diabetes within a specific timeframe. The diabetes prediction module 120 utilizes supervised machine learning models trained on annotated health datasets to ensure accuracy and adaptability. The outcome supports personalized health recommendations, early interventions, and continuous monitoring, enabling proactive diabetes prevention and management.
[0068] In one embodiment of the present invention, the diabetes prediction module 120 utilizes machine learning models selected from a group comprising of Decision Tress, Random Forest, XGBoost, Support Vector Machines (SVM), and Neural Networks.
[0069] The feedback module 122 is configured to provide real-time feedback and generate personalized recommendations for dietary intake, physical activity, and behavioural modifications based on the predicted diabetes risk score. These recommendations are dynamically tailored to the user’s health profile and may include suggestions for optimizing dietary intake such as reducing sugar or increasing fibre consumption, modifying physical activity routines, and adopting healthier behavioural patterns including improved sleep hygiene and stress management techniques. The feedback module 122 leverages both the extracted features and predictive insights to ensure that guidance is relevant, actionable, and aligned with the user's current lifestyle, thereby supporting proactive health management and minimizing diabetes risk progression.
[0070] The alert generation module 124 is configured to generate preventive health alerts upon detection of a risk score exceeding a predefined threshold value. These alerts serve as early warnings, prompting the user to take timely action and adopt preventive measures to curb the possibility of diabetes.
[0071] In one embodiment of the present invention, the alert generation module 124 generates alerts including prompts for medical consultation, behavioural adjustments, or immediate glucose level checks based on the risk score and trend analysis.
[0072] In one embodiment of the present invention, the alert generation module 124 may generate alerts in the form of notifications, messages, and visual indicators, ensuring immediate awareness.
[0073] In one embodiment of the present invention, the microprocessor 108 further comprises a data security and sharing module 126 configured to manage secure storage and permission-controlled sharing of health data using a blockchain-based framework. The data security and sharing module 126 ensures that all user data such as genetic information, medical history, wearable sensor outputs, and lifestyle-related inputs is encrypted and recorded in tamper-proof, time-stamped blocks. Access to this data is governed by user-defined permissions, allowing only authorized parties, such as healthcare providers, to retrieve relevant information. The data security and sharing module 126 enhances data integrity, traceability, and user privacy while enabling transparent and secure data exchange across healthcare stakeholders.
[0074] The output module 128 is configured to transmit processed insights, alerts, and recommendations to the user. The output module 128 is responsible for presenting diabetes risk prediction and health statistics to the user on the user interface 110 in a clear and user-friendly manner.
[0075] The user interface 110 is connected to the microprocessor 108 via the communication network 104 and is configured to serve as the primary point of interaction between the user and the system 100. The user interface 110 enables users to input personal and lifestyle information manually, view real-time updates of their diabetes risk score and classification, and receive personalized feedback in the form of recommendations for dietary habits, physical activity, and behaviour modification. The user interface 110 further displays preventive health alerts and allows access to interactive health dashboards that visualize trends, progress, and historical data. The user interface 110 may also provide downloadable health reports and facilitate communication with healthcare professionals.
[0076] In one embodiment of the present invention, the user interface 110 further comprises a chatbot configured to provide personalized coaching and recommendations on diet, exercise, and health management based on the user's health data and risk assessment. The chatbot is configured to interact with the users in a conversational manner, enabling real-time engagement and support. The chatbot can answer user queries, interpret input data, and provide personalized recommendations related to diet, exercise, sleep, stress management, and other lifestyle factors. The chatbot utilizes NLP and machine learning algorithms to understand user context, adapt responses accordingly, and deliver proactive coaching.
[0077] In one embodiment of the present invention, the chatbot is an AI-based virtual assistant.
[0078] In one embodiment of the present invention, the system 100 further comprises a cloud database 106 configured to store user-health related data, prediction results, and personalized recommendations for secure access, retrieval, and progressive health monitoring. The cloud database 106 helps in data storage and easy retrieval of health-related data and generated prediction results. The cloud database 106 ensures that all data is centrally organized, readily accessible to authorized components for real-time processing, and supports scalability and continuity of care by enabling progressive health monitoring across time.
[0079] FIG. 2 illustrates a flowchart of a method 200, outlining the sequential steps for predicting diabetes and managing health proactively, in accordance with an exemplary embodiment of the present disclosure.
[0080] The method 200 may include, at step 202, collecting user-health related data from a plurality of sources via a data acquisition unit 102, at step 204, transmitting data between the various components of the system 100 via a communication network 104, at step 206, processing health related data for diabetes prediction via a microprocessor 108 comprising of several modules, at step 208, receiving input data from the data acquisition unit 102 via a data input module 112, at step 210, cleaning, normalizing, and pre-processing the received multi-format data to prepare the data for subsequent analysis via a data processing module 114, at step 212, extracting relevant features from the processed text and image data via a feature extraction module 116, at step 214, classifying the user into predefined risk classes based on the extracted features via a classification module 118, at step 216, determining a user-specific diabetes risk score and predict the likelihood of developing diabetes based on the classified data utilizing machine learning models via a diabetes prediction module 120, at step 218, providing real-time feedback and generate personalized recommendations for dietary intake, physical activity, and behavioural modifications based on the predicted diabetes risk score via a feedback module 122, at step 220, generating preventive health alerts upon detection of a risk score exceeding a predefined threshold value via an alert generation module 124, at step 222, transmitting processed insights, alerts, and recommendations to the user via an output module 128, and at step 224, interacting with the user to receive manual inputs, display risk levels, recommendations and alerts, and provide access to personalized health dashboards and reports via a user interface 110.
[0081] In the best mode of operation, a user begins using the intelligent diabetes prediction system 100 by registering on the user interface 110 of the system 100 through the mobile or web-based application. The data acquisition unit 102, which is embedded within the backend infrastructure of the system 100, continuously collects data from multiple sources. This includes physiological signals like heart rate, sleep duration, and activity levels from smartwatches or fitness trackers, as well as lifestyle-related inputs such as diet and stress levels via text inputs entered by the user, and meal images captured by the user using their phone camera. The user may also upload genetic test results which provide variant data associated with diabetes risk and historical medical records which provide user health history. All this multi-source data is seamlessly collected and transmitted through the communication network 104 to the microprocessor 108, which is hosted on the cloud database 106. Once the data is received by the data input module 112, it is sent to the data processing module 114, where it undergoes cleaning, normalization, and structuring to ensure it is ready for analysis. The feature extraction module 116 then extracts key features such as caloric values from meal images, indicators of stress from text, or trends in physical activity and forwards them to the classification module 118, which places the user into a risk class like low, moderate, or high. The diabetes prediction module 120 then computes a personalized diabetes risk score using advanced machine learning algorithms based on these features and the classified risk level. The feedback module 122 simultaneously provides personalized recommendations such as increasing daily steps, modifying meal composition, or improving sleep habits. If the score crosses a predefined threshold, the alert generation module 124 notifies the user immediately via the user interface 110, prompting medical consultation or lifestyle changes. All health records and interactions are securely handled, end-to-end encrypted, and stored through the data security and sharing module 126, ensuring privacy and user control over their sensitive data. Personalized feedback and preventive alerts are delivered through the user interface 110, which also provides access to a dynamic health dashboard and facilitates interactive communication with an AI-based chatbot for real-time feedback and health guidance. Based on the insights of the system 100, the user can implement recommended lifestyle changes or consult a medical professional for further support.
[0082] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0083] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0084] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0085] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0086] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. An intelligent health monitoring system (100) for early diabetes prediction and proactive health management, the system (100) comprising:
a data acquisition unit (102) configured to collect user-health related data from a plurality of sources;
a communication network (104) configured to transmit data between the various components of the system (100);
a microprocessor (108) connected to the data acquisition unit (102) via the communication network (104) and configured to process health related data for diabetes prediction, wherein the microprocessor (108) further comprises:
a data input module (112) configured to receive input data from the data acquisition unit (102);
a data processing module (114) configured to clean, normalize, and pre-process the received multi-format data to prepare the data for subsequent analysis;
a feature extraction module (116) configured to extract relevant features from the processed text and image data;
a classification module (118) configured to classify the user into predefined risk classes based on the extracted features;
a diabetes prediction module (120) configured to determine a user-specific diabetes risk score and predict the likelihood of developing diabetes based on the classified data utilizing machine learning models;
a feedback module (122) configured to provide real-time feedback and generate personalized recommendations for dietary intake, physical activity, and behavioural modifications based on the predicted diabetes risk score;
an alert generation module (124) configured to generate preventive health alerts upon detection of a risk score exceeding a predefined threshold value;
an output module (128) configured to transmit processed insights, alerts, and recommendations to the user; and
a user interface (110) connected to the microprocessor (108) via the communication network (104) and configured to interact with the user to receive manual inputs, display risk levels, recommendations and alerts, and provide access to personalized health dashboards and reports.
2. The system (100) as claimed in claim 1, wherein the data acquisition unit (102) collects user-health related data from a plurality of sources, including but not limited to clinical genetic reports, historical medical records, real-time physiological data obtained from wearable sensors, nutritional related data obtained from user-captured meal images, and lifestyle-related data obtained from user-entered text inputs and user-captured images.
3. The system (100) as claimed in claim 2, wherein the real-time physiological data obtained from wearable sensors comprises data, including but not limited to glucose levels, heart rate, body temperature, sleep patterns, blood oxygen levels, physical activity metrics, and stress indicators.
4. The system (100) as claimed in claim 2, wherein the nutritional related data obtained from user-captured meal images comprises data, including but not limited to estimations of sugar, carbohydrate, fat, protein, and calorie content.
5. The system (100) as claimed in claim 2, wherein the lifestyle-related data obtained from user-entered text inputs and user-captured images comprises data, including but not limited to hydration levels, dietary habits, physical activity, sleep patterns, stress indicators, and daily routine behaviours.
6. The system (100) as claimed in claim 1, wherein the classification module (118) classifies the user into predefined risk classes, including but not limited to low, moderate, and high diabetes risk, based on the extracted features.
7. The system (100) as claimed in claim 1, wherein the microprocessor (108) further comprises a data security and sharing module (126) configured to manage secure storage and permission-controlled sharing of health data using a blockchain-based framework.
8. The system (100) as claimed in claim 1, wherein the user interface (110) further comprises a chatbot configured to provide personalized coaching and recommendations on diet, exercise, and health management based on the user's health data and risk assessment.
9. The system (100) as claimed in claim 1, wherein the system (100) further comprises a cloud database (106) configured to store user-health related data, prediction results, and personalized recommendations for secure access, retrieval, and progressive health monitoring.
10. A method (200) for predicting diabetes and managing health proactively, the method (200) comprising:
collecting user-health related data from a plurality of sources via a data acquisition unit (102);
transmitting data between the various components of the system (100) via a communication network (104);
processing health related data for diabetes prediction via a microprocessor (108) comprising of several modules;
receiving input data from the data acquisition unit (102) via a data input module (112);
cleaning, normalizing, and pre-processing the received multi-format data to prepare the data for subsequent analysis via a data processing module (114);
extracting relevant features from the processed text and image data via a feature extraction module (116);
classifying the user into predefined risk classes based on the extracted features via a classification module (118);
determining a user-specific diabetes risk score and predict the likelihood of developing diabetes based on the classified data utilizing machine learning models via a diabetes prediction module (120);
providing real-time feedback and generate personalized recommendations for dietary intake, physical activity, and behavioural modifications based on the predicted diabetes risk score via a feedback module (122);
generating preventive health alerts upon detection of a risk score exceeding a predefined threshold value via an alert generation module (124);
transmitting processed insights, alerts, and recommendations to the user via an output module (128); and
interacting with the user to receive manual inputs, display risk levels, recommendations and alerts, and provide access to personalized health dashboards and reports via a user interface (110).