E-Healthcare Monitoring System Using ML

Abstract

The challenge lies with accessibility to accurate health information and personalized health advice, which are correspondingly bedeviled by generic information and limited access in remote localities. This leads to massive occurrences of self-diagnosis, misdiagnosis, improper self-care, and bad health results. The proposed solution to the problem is the design and implementation of an \"E-Health Care Monitoring System,\" where individuals will enter their symptoms and receive detailed information on suspected diseases, together with descriptions, precautions, medication suggestions, workout plans, and diet recommendations. Users would thus be empowered to make health decisions, sense an order of being healthy, and submit to improved health outcomes. The system will also be developed to ease access through a user-friendly interface and multilingual support to truly make it inclusive. Further, integration with wearable and health-tracking instruments will allow real-time monitoring and give personalized recommendations. Data analytics would be used along with machine learning to further the continuous improvement of the accuracy and relevance of health insights.

Introduction

Access to accurate, personalized healthcare information remains challenging, especially for rural and underserved populations, leading to misdiagnosis and poor health outcomes. To address this, the project develops an E-Health Care Monitoring System that enables users to input symptoms, predict possible diseases using machine learning, and receive personalized lifestyle, medication, diet, and exercise recommendations. The system is multilingual and user-friendly to ensure broad accessibility.

It integrates real-time data from wearable devices to enhance accuracy and personalizes health insights through continuous data analytics and learning. The platform aims to empower individuals with timely health advice and promote proactive, healthier living.

The system architecture features a modular three-tier design: a user-friendly frontend, a backend processing layer with machine learning for disease prediction, and a data layer that integrates wearable device information. Testing showed high accuracy (~85–90%) in predicting common illnesses, positive user feedback on accessibility and usability, and successful real-time wearable integration with health alerts.

This solution enhances healthcare accessibility, especially in low-resource areas, promotes self-care and informed decision-making, and supports increased health literacy for diverse users.

Conclusion

The e-healthcare Care Monitoring System tries to fill the gap by providing more accessible and individualized health care, particularly to people who may have little access to professional medical services. Their findings and personalized recommendations are based on machine learning algorithms\' analyses of user information about diet plans, exercise programs, and preventive care. Unique to the system is its adaptive learning mechanism. The system \"learns\" gradually through user interaction and feedback, allowing the recommendations to become progressively more accurate and relevant, thereby increasing the efficiency of the health guidance. As for the usability, privacy, and security of data, the system was built with a very strong focus. Thus, it is reliable and can also hold the respect of a large category of users. This way, it does introduce self-care among the people by making them actively participate in monitoring their health and choosing how to function or live. In conclusion, the E-healthcare Care Monitoring System thus improves health outcomes by lowering the risk of chronic diseases and achieving the shared vision of making quality healthcare guidance available to all, irrespective of geographical or socioeconomic barriers.

References

[1] Poudel, Kushal. (2022). \"PATIENT HEALTH MONITORING SYSTEM\". [2] D. S. R. Krishnan, S. C. Gupta and T. Choudhury, \"An IoT based Patient Health Monitoring System,\" 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE), Paris, France, 2018, pp. 01-07, doi: 10.1109/ICACCE.2018.8441708. keywords: {Monitoring;Medical services;Temperature sensors;Biomedical monitoring;Temperature measurement;Insurance;Internet of Things;Healthcare;Services;Applications;Technologies;Architectures;Atmega}, [3] Mohammad Monirujjaman Khan, MehediMasud, Sami Bourouis, Mohammad Shorfuzzaman, \"IoT-Based Smart Health Monitoring System for Elderly People\" ,Wireless Communications and Mobile Computing,DOI: 10.1155/2022/9639195 2022 [4] Abdulmalek, S.; Nasir, A.; Jabbar, W.A.; Almuhaya, M.A.M.; Bairagi, A.K.; Khan, M.A.-M.; Kee, S.-H. IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review. Healthcare 2022, 10, 1993.https://doi.org/10.3390/healthcare10101993. [5] De Croon R, Van Houdt L, Htun NN, Štiglic G, VandenAbeele V, Verbert K. Health Recommender Systems: Systematic Review. J Med Internet Res. 2021 Jun 29;23(6):e18035. doi: 10.2196/18035. PMID: 34185014; PMCID: PMC8278303. [6] Nnamdi, Maureen. (2024). Predictive Analytics in Healthcare. [7] Keesara, Sirina& Jonas, Andrea & Schulman, Kevin. (2020). Covid-19 and Health Care’s DigitalRevolution. New England Journal of Medicine. 382. 10.1056/NEJMp2005835. [8] National Research Council (US) Committee on Maintaining Privacy and Security in Healthcare Applications of the National Information Infrastructure. For the Record Protecting Electronic Health Information. Washington (DC): National Academies Press (US); 1997. 3, Privacy and Security Concerns Regarding Electronic Health Information. Available from: https://www.ncbi.nlm.nih.gov/books/NBK233428/ [9] P. Chinnasamy, Wing-Keung Wong, A. Ambeth Raja, Osamah Ibrahim Khalaf, AjmeeraKiran, J. ChinnaBabu,Health Recommendation System using Deep Learning-based Collaborative Filtering, Heliyon, Volume 9, Issue 12, 2023, e22844, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2023.e22844. [10] Cai Y, Yu F, Kumar M, Gladney R, Mostafa J. Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review. Int J Environ Res Public Health. 2022 Nov 16;19(22):15115. doi: 10.3390/ijerph192215115. PMID: 36429832; PMCID: PMC9690602. [11] P. Kumar and A. Kumar, \"A Review of Healthcare Recommendation Systems Using Several Categories of Filtering and Machine Learning-Based Methods,\" 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, 2022, pp. 762-768, doi: 10.1109/ICCCIS56430.2022.10037604. keywords: {Learning systems;Analyticalmodels;Privacy;Scalability;Neuralnetworks;Medicalservices;Real-timesystems;Machine learning methods (ML);singular value decomposition (SVD);convolution neural network (CNN);Content-based (CB);healthcare recommendation system (RS)}, [12] Canali S, Schiaffonati V, Aliverti A. Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness. PLOS Digit Health. 2022 Oct 13;1(10):e0000104. doi: 10.1371/journal.pdig.0000104. PMID: 36812619; PMCID: PMC9931360.

Copyright

Copyright © 2025 Bhoopathy R, Janaprasath G, Rahul M, Dheenadhayalan R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.