Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to a disease prediction system and particularly to an early disease prediction system.
Description of Related Art
[002] In recent years, wearable devices and mobile health applications have gained popularity for monitoring health-related parameters. Products such as fitness trackers and smartwatches measure heart rate, oxygen levels, and physical activity, while online health platforms provide access to virtual consultations. These technologies demonstrate an increasing reliance on digital solutions for preventive healthcare and remote medical support.
[003] Artificial intelligence has approached the healthcare domain with systems developed to assist in medical diagnosis. Platforms from leading technology providers employ AI models for data analysis and disease detection. However, these systems often rely on limited datasets and face challenges in terms of generalizability, interpretability, and practical deployment in real-world healthcare settings.
[004] Despite these advancements, existing methods do not provide adequate reliability or accessibility for comprehensive health management. Current systems remain restricted by concerns related to data privacy, model complexity, and performance in complex environments.
[005] There is thus a need for an improved and advanced early disease prediction system that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide an early disease prediction system. The disease prediction system comprising a wearable device adapted to fetch health data points from a subject. The disease prediction system further comprising a gateway device adapted to receive the fetched health data points from the wearable device, and upload the fetched health data to a cloud server. The disease prediction system further comprising an Artificial Intelligence (AI) based control unit, established in the cloud server. The Artificial Intelligence (AI) based control unit is configured to access the health data points stored in the cloud server; process the health data points with at least one trained machine learning model to predict a potential disease associated with the subject; generate personalized health recommendations including medications, precautionary measures, diet plans, exercise plans, or a combination thereof based on the predicted potential disease; continuously monitor the health data points in a real-time for a dynamic updating of the predicted potential disease and the personalized health recommendations in accordance with detected changes; and provide the predicted potential disease and the personalized health recommendations through a user interface, installed on the gateway device.
[007] Embodiments in accordance with the present invention further provide a method for early disease prediction using an early disease prediction system. The method comprising steps of fetching health data points from a subject; receiving the fetched health data points onto a gateway device, from the wearable device; uploading the fetched health data to a cloud server; accessing the health data points stored in the cloud server; processing the health data points with at least one trained machine learning model to predict a potential disease associated with a subject; generating personalized health recommendations including medications, precautionary measures, diet plans, exercise plans, or a combination thereof based on the predicted potential disease; continuously monitoring the health data points in a real-time for a dynamic updating of the predicted potential disease and the personalized health recommendations in accordance with detected changes; and providing the predicted potential disease and the personalized health recommendations through a user interface, installed on the gateway device.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide an early disease prediction system.
[009] Next, embodiments of the present application may provide a disease prediction system that identifies potential health risks at an early stage through predictive analytics.
[0010] Next, embodiments of the present application may provide a disease prediction system that enables timely preventive measures and reduces a likelihood of severe complications.
[0011] Next, embodiments of the present application may provide a disease prediction system that generates individualized recommendations that include medications, diet, exercise, and precautionary steps tailored to a health profile of a subject.
[0012] Next, embodiments of the present application may provide a disease prediction system that ensures that health plans dynamically adapt to changing conditions or the subject.
[0013] Next, embodiments of the present application may provide a disease prediction system that maintains accuracy and relevance.
[0014] Next, embodiments of the present application may provide a disease prediction system that allows the subject to access healthcare support remotely.
[0015] Next, embodiments of the present application may provide a disease prediction system that eliminates a need for frequent hospital visits and expensive diagnostic procedures.
[0016] Next, embodiments of the present application may provide a disease prediction system that assists doctors in improving diagnostic efficiency.
[0017] Next, embodiments of the present application may provide a disease prediction system that reduces manual errors, and enhances overall treatment outcomes.
[0018] These and other advantages will be apparent from the present application of the embodiments described herein.
[0019] 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.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0021] FIG. 1 illustrates a schematic block diagram of an early disease prediction system, according to an embodiment of the present invention; and
[0022] FIG. 2 depicts a flowchart of a method for early disease prediction using an early disease prediction system, according to an embodiment of the present invention.
[0023] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0024] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0025] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0026] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0027] As used herein, the term “Subject” refers to a human, an animal, or any living entity from whom health data points are obtained for processing, analysis, and prediction.
[0028] As used herein, the term “Medical professional” refers to a qualified individual authorized to practice healthcare, including but not limited to a physician, surgeon, nurse, clinician, medical specialist, or any licensed healthcare provider, who evaluates, monitors, or manages the health condition of a subject.
[0029] FIG. 1 illustrates a schematic block diagram of an early disease prediction system 100 (hereinafter referred to as the disease prediction system 100), according to an embodiment of the present invention. The disease prediction system 100 may be an Artificial Intelligence-powered solution designed to predict diseases and provide customized health plans, addressing a problem of delayed diagnosis and lack of personalized healthcare recommendations. The disease prediction system 100 may integrate machine learning (ML), real-time health monitoring, and a dynamic web-based frontend to enhance early disease detection and proactive health management. The disease prediction system 100 may uniquely integrate machine learning with real-time health monitoring from a wearable device 102. The disease prediction system 100 dynamically adapts to personalized disease predictions and health plans based on user data.
[0030] According to the embodiments of the present invention, the disease prediction system 100 may incorporate non-limiting hardware components to enhance a processing speed and an efficiency such as the disease prediction system 100 may comprise the wearable device 102, a gateway device 104, a user interface 106, a cloud server 108, an Artificial Intelligence (AI) based control unit 110, and a trained machine learning model 112. In an embodiment of the present invention, the hardware components of the disease prediction system 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing systems.
[0031] In an embodiment of the present invention, the wearable device 102 may be adapted to fetch health data points from a subject. The health data points may be, but not limited to, a blood pressure, a body temperature, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the health data points, including known, related art, and/or later developed technologies. The wearable device 102 may be, but not limited to, a ring, a watch, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the wearable device 102, including known, related art, and/or later developed technologies.
[0032] In an embodiment of the present invention, the gateway device 104 may be adapted to receive the fetched health data points, from the wearable device 102 and upload the fetched health data to the cloud server 108. The gateway device 104 may be adapted to receive additional health data from a symptom-disease dataset, an Internet of Things (IoT) sensor, lifestyle parameters, a genetic predisposition record, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of additional source, including known, related art, and/or later developed technologies. The gateway device 104 may be, a laptop, a smartphone, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the gateway device 104, including known, related art, and/or later developed technologies.
[0033] The gateway device 104 may comprise the user interface 106. The user interface 106 may enable the subject to manually input the health data points. The user interface 106 may further enable a demography to view a predicted potential disease and a personalized health recommendations. The user interface 106 may further be accessed by the demography such as, but not limited to, a subject, a medical professional, a common individual, a group of individuals, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the demography, including known, related art, and/or later developed technologies. The user interface 106 may be a web-based application. The web-based application may be developed using a Flask for a back end interface and Bootstrap for a front end interface.
[0034] The user interface 106 may enable the subject to access health reports, receive instant alerts, and track progress over time. The user interface 106 may be based on Python programming language, and may comprise a dashboard for an interactive data visualization. Embodiments of the present invention are intended to include or otherwise cover any tools and computer programming languages for development and establishment of the user interface 106, including known, related art, and/or later developed technologies.
[0035] In an embodiment of the present invention, an Artificial Intelligence (AI) based control unit 110 may be established in the cloud server 108. The Artificial Intelligence (AI) based control unit 110 may be configured to access the health data points stored in the cloud server 108. The health data points may be delivered from the wearable device 102, onto the cloud server 108 when the subject may log in to the user interface 106. The user interface 106 may enable the subject to provide the additional health data. The additional health data may be provided by the subject onto an easy-to-use form. The Artificial Intelligence (AI) based control unit 110 may be configured to preprocess the health data points by cleaning, normalizing, and encoding to enhance efficiency of the trained machine learning model 112.
[0036] The Artificial Intelligence (AI) based control unit 110 may be configured to process the health data points with at least one trained machine learning model 112 to predict potential diseases associated with the subject. The machine learning model may be, but not limited to, a Support Vector Classifier (SVC), a Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), a K-Nearest Neighbours Classifier (k-NN), a Multinomial Naïve Bayes (MNB), and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of trained machine learning model 112, including known, related art, and/or later developed technologies. The trained machine learning model 112 may learn from a historical record and symptom correlations to predict diseases with high accuracy.
[0037] The Artificial Intelligence (AI) based control unit 110 may be configured to generate the personalized health recommendations, including medications, precautionary measures, diet plans, exercise plans, and so forth, based on the predicted potential disease. Embodiments of the present invention are intended to include or otherwise cover any type of the personalized health recommendations, including known, related art, and/or later developed technologies.
[0038] The Artificial Intelligence (AI) based control unit 110 may be configured to continuously monitor the health data points in a real-time for a dynamic updating of the predicted potential disease and the personalized health recommendations in accordance with detected changes.
[0039] The Artificial Intelligence (AI) based control unit 110 may be configured to provide the predicted potential disease and the personalized health recommendations through the user interface 106, installed on the gateway device 104.
[0040] FIG. 2 depicts a flowchart of a method 200 for early disease prediction using the disease prediction system 100, according to an embodiment of the present invention.
[0041] At step 202, the disease prediction system 100 may fetch the health data points from the subject.
[0042] At step 204, the disease prediction system 100 may receive the fetched health data points onto the gateway device 104 from the wearable device 102.
[0043] At step 206, the disease prediction system 100 may upload the fetched health data to the cloud server 108.
[0044] At step 208, the disease prediction system 100 may access the health data points stored in the cloud server 108.
[0045] At step 210, the disease prediction system 100 may preprocess the health data points by cleaning, normalizing, and encoding to enhance the efficiency of the trained machine learning model 112.
[0046] At step 212, the disease prediction system 100 may process the health data points with the trained machine learning model 112 to predict the potential diseases associated with the subject.
[0047] At step 214, the disease prediction system 100 may generate personalized health recommendations, including the medications, the precautionary measures, the diet plans, the exercise plans, and so forth, based on the predicted potential disease.
[0048] At step 216, the disease prediction system 100 may continuously monitor the health data points in the real-time for the dynamic updating of the predicted potential disease and the personalized health recommendations in accordance with the detected changes.
[0049] At step 218, the disease prediction system 100 may provide the predicted potential disease and the personalized health recommendations through the user interface 106, installed on the gateway device 104.
[0050] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to 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.
[0051] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. An early disease prediction system (100), the disease prediction system (100) comprising:
a wearable device (102) adapted to fetch health data points from a subject;
a gateway device (104) adapted to receive the fetched health data points from the wearable device (102), and upload the fetched health data to a cloud server (108); and
an Artificial Intelligence (AI) based control unit (110), established in the cloud server (108), characterized in that the Artificial Intelligence (AI) based control unit (110) is configured to:
access the health data points stored in the cloud server (108);
process the health data points with at least one trained machine learning model (112) to predict a potential disease associated with the subject;
generate personalized health recommendations including medications, precautionary measures, diet plans, exercise plans, or a combination thereof, based on the predicted potential disease;
continuously monitor the health data points in a real-time for a dynamic updating of the predicted potential disease and the personalized health recommendations in accordance with detected changes; and
provide the predicted potential disease and the personalized health recommendations through a user interface (106), installed on the gateway device (104).
2. The disease prediction system (100) as claimed in claim 1, wherein the Artificial Intelligence (AI) based control unit (110) is configured to preprocess the health data points by cleaning, normalizing, and encoding to enhance an efficiency of the trained machine learning model (112).
3. The disease prediction system (100) as claimed in claim 1, wherein the trained machine learning model (112) is selected from a Support Vector Classifier (SVC), a Random Forest Classifier (RFC), a Gradient Boosting Classifier (GBC), a K-Nearest Neighbours Classifier (k-NN), a Multinomial Naïve Bayes (MNB), or a combination thereof.
4. The disease prediction system (100) as claimed in claim 1, wherein the gateway device (104) is adapted to receive additional health data from a symptom-disease dataset, an Internet of Things (IoT) sensor, lifestyle parameters, a genetic predisposition record, or a combination thereof.
5. The disease prediction system (100) as claimed in claim 1, wherein the user interface (106) is accessed by a demography selected from the subject, a medical professional, a common individual, a group of individuals, or a combination thereof.
6. A method (200) for early disease prediction using an early disease prediction system (100), the method (200) is characterized by steps of:
fetching health data points from a subject;
receiving the fetched health data points onto a gateway device (104), from the wearable device (102);
uploading the fetched health data to a cloud server (108);
accessing the health data points stored in the cloud server (108);
processing the health data points with at least one trained machine learning model (112) to predict a potential disease associated with the subject;
generating personalized health recommendations including medications, precautionary measures, diet plans, exercise plans, or a combination thereof based on a predicted potential disease;
continuously monitoring the health data points in a real-time for a dynamic updating of the predicted potential disease and the personalized health recommendations in accordance with detected changes; and
providing the predicted potential disease and the personalized health recommendations through a user interface (106), installed on the gateway device (104).
7. The method (200) as claimed in claim 6, comprising a step of preprocessing the health data points by cleaning, normalizing, and encoding to enhance efficiency of the trained machine learning model (112).
8. The method (200) as claimed in claim 6, wherein the trained machine learning model (112) are selected from a Support Vector Classifier (SVC), a Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), a K-Nearest Neighbours Classifier (k-NN), a Multinomial Naïve Bayes (MNB), or a combination thereof.
9. The method (200) as claimed in claim 6, wherein the gateway device (104) is adapted to receive additional health data from a symptom-disease dataset, an Internet of Things (IoT) sensor, lifestyle parameters, a genetic predisposition record, or a combination thereof.
10. The method (200) as claimed in claim 6, wherein the user interface (106) is accessed by a demography selected from a subject, a medical professional, a common individual, a group of individuals, or a combination thereof.
Date: October 08, 2025
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant