Description:[0001] The present invention is related to the Healthcare Technology of the Field Remote Patient Monitoring, IoT (Internet of Things), Machine Learning, majorly of computer science.
Background
[0002] With the proliferation of connected devices, healthcare has seen a surge in IoT applications. These devices enable continuous monitoring of patient health metrics remotely, facilitating early intervention and personalized care.
[0003] Remote monitoring allows healthcare providers to keep track of patients' health status outside traditional clinical settings. This is particularly valuable for managing chronic conditions, post-operative care, and elderly patient monitoring, improving patient outcomes and reducing healthcare costs.
[0004] Machine learning algorithms play a crucial role in analyzing the vast amount of data generated by IoT devices. These algorithms can identify patterns, predict health deterioration, and provide insights for personalized interventions, contributing to precision health management.
[0005] The convergence of IoT and machine learning in remote healthcare monitoring holds promise for transforming healthcare delivery. It offers opportunities for early detection of health issues, proactive intervention, and improved patient engagement, ultimately leading to better health outcomes and enhanced quality of life.
[0006] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.

[0007] In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[0008] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0009] The recitation of ranges of values herein is merely intended toserve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performedin any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non- claimed element essential to the practice of the invention.

[0010] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.
Objects of the Invention
[0011] These are physical objects embedded with sensors and connectivity capabilities, such as wearable fitness trackers, smartwatches, and home health monitoring systems. IoT devices collect real-time data on various health parameters, including heart rate, blood pressure, glucose levels, and activity levels.
[0012]. These are computational objects designed to analyze and interpret the data collected by IoT devices. Machine learning models utilize algorithms to identify patterns, anomalies, and trends in the data, enabling predictive analytics and decision support for healthcare providers.
Brief Description of the Drawing
[0013] The figure 1 represents working model in the present invention with its prototype.
Detailed Description:
[0014] In figure 1, showing the input parameter; which is to be processed by the system 100.
[0015] Determine specific healthcare scenarios where remote monitoring can provide significant benefits, such as managing chronic diseases like diabetes or hypertension, post-operative care, or elderly patient monitoring. [0016] Choose appropriate IoT devices based on the identified use cases. Consider factors such as the type of health data to be collected, device accuracy, patient comfort, and ease of integration with existing healthcare systems.
[0017] Deploy selected IoT devices to collect patient health data continuously. Establish secure data transmission protocols to ensure patient privacy and compliance with healthcare regulations. Integrate IoT data streams with electronic health records (EHR) or other healthcare information systems for centralized monitoring and analysis.
[0018] Cleanse and preprocess the collected IoT data to remove noise, handle missing values, and standardize formats. This step ensures data quality and prepares the dataset for analysis by machine learning algorithms.
[0019] Develop machine learning models tailored to the specific healthcare use cases. Choose appropriate algorithms such as classification, regression, or anomaly detection based on the nature of the health data and the desired insights. Train the models using historical data to learn patterns and correlations.
[0020] Deploy trained machine learning models to analyze incoming IoT data in real-time. Use predictive analytics to detect early signs of health deterioration, identify abnormal patterns, and forecast future health outcomes. Integrate model predictions into clinical workflows to enable timely interventions by healthcare providers.
[0021] Continuously monitor the performance of deployed IoT devices and machine learning models in real-world healthcare settings. Gather feedback from healthcare providers and patients to identify areas for improvement and fine-tune the system accordingly. Iterate on the implementation process to optimize precision health insights and enhance patient outcomes over time.
[0022] In an aspect, any or a combination of machine learning mechanisms such as decision tree learning, Bayesian network, deep learning, random forest, supervised vector machines, reinforcement learning, prediction models, Statistical Algorithms, Classification, Logistic Regression, Support Vector Machines, Linear Discriminant Analysis, K- Nearest Neighbours, Decision Trees, Random Forests, Regression, Linear Regression, Support Vector Regression, Logistic Regression, Ridge Regression, Partial Least-Squares Regression, Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering
– Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis

Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF), Kernel PCA, Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (kernel trick again), Ensemble Algorithms, Deep Learning, Reinforcement Learning, AutoML (Bonus) and the like can be employed to learn sensor/hardware components.
[0023] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[0024] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-
exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements,
components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C …. and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
, Claims:1. Precision Health Insights through IoT and Machine Learning Lead to Early Disease Detection: By continuously monitoring patient health data using IoT devices and analyzing it with machine learning algorithms, healthcare providers can detect subtle changes in health parameters, enabling early identification of diseases or health complications before they progress to advanced stages.
2. Remote Healthcare Monitoring Enhances Patient Engagement and Empowerment: Remote monitoring facilitated by IoT devices allows patients to actively participate in their own healthcare management. Through access to real-time health data and personalized insights generated by machine learning models, patients gain greater awareness of their health status, leading to increased engagement in self-care activities and better adherence to treatment plans.
3. Integration of IoT and Machine Learning Optimizes Healthcare Resource Utilization: By harnessing IoT for remote monitoring and machine learning for predictive analytics, healthcare providers can optimize resource allocation and improve operational efficiency. Proactive identification of high-risk patients and timely intervention based on predictive insights help prevent avoidable hospitalizations and emergency department visits, reducing healthcare costs and freeing up resources for more critical cases.