Development of a Model for Health Monitoring Using Machine Learning and Data Mining System

Ugbor Gideon Tochi; Amanze Bethran Chibuike; Igbe Chijioke

doi:10.56201/ijcsmt.v11.no2.2025.pg154.163

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Development of a Model for Health Monitoring Using Machine Learning and Data Mining System

Ugbor Gideon Tochi, Amanze Bethran Chibuike, Igbe Chijioke

Abstract

Mobile Health is an emerging field that allows for real-time monitoring of individuals between routine clinical visits. It also makes it possible to remotely gather health signals, track disease progression and provide just-in-time interventions. Data Mining is the process of extracting valid, previously unknown, comprehensible and actionable information from large databases and using it to make crucial, critical business and logical decisions. The motivation is to improve on the existing by designing a medical bio-card, where the patient’s medical history can be accessed by any readily available medical personnel, other than the registered one (probably due to network issues or remote location), to proscribe drugs and commence treatment when the patient is in critical condition. The Objective of this research is to improve on the current system that focused on key risk factors such as High/ low Blood Pressure (H/LBP), Sugar level, Blood level, High Body Temperature (Fever), Tobacco use, Alcohol use, Inadequate Physical Inactivity, Unhealthy Diets, Abnormal Sleep Patterns etc which plays key roles in many chronic diseases. Hence, there is a need to continuously monitor at risk individuals for their health status and activities over extended periods of time in their natural settings with the goal of improving their health and well being using wearable sensors. Wearable devices are devices worn on, in or around the body, and it has highly reduced form factors, longer battery life and enhanced network capabilities to continuously and remotely monitor individuals in their natural settings over extended period of time. The Methodology employed for the study of the existing system and adopted for the design of the new system is Object-Oriented Analysis and Design Method (OOADM) since it is object-oriented in nature. The improved system implemented with an accelerometer, gyroscope and/or magnetometer will detect motion, orientation

Keywords

PatientsDoctorsdata mining and hospitals

References

[1]. Chen, J.H; & Asch, S.M. (2017), Machine Learning and Prediction in Medicine Beyond the Peak of Inflated Expectations. New England Journal of Medicine, 376, 2507-2509. [2]. Banaee, H; Ahmed, M.U & Loutfi A. (2013), Data Mining for Wearable Sensors in Health Monitoring Systems: A review of recent trends and challenges. Sensors, 13(12), 17472-17500. [3]. Rajkomar, A; Dean, J. & Kohana, I. (2019), Machine Learning in Medicine. New England Journal of Medicine, 380, 1347-1358. [4].Zhou, X; Lu, Y; Chen, J & Li, Z. (2019), Health Monitoring and Management Using Internet of Things (IoT) Sensing with Cloud-based Processing: Opportunities and Challenges. IEEE International Conference on Services Computing. [5]. Pappachan, B; Ravindrain, P; & Nair, P. (2019), Secure and Scalable Blockchain Framework for Health Monitoring using Wearable Medical Devices. IEEE International Conference on Communication Systems and Networks. [6]. Baig, M.M; GholamHosseini, H; & Connolly, M.J (2015), Mobile Healthcare Application Systems Design Review, Critical Issues and Challenges. Australasian Physical and Engineering Sciences in Medicine, 38, 23-38. [7]. Choi, E; Schuetz, A; Stewart, W.F & Sun, J. (2017), Using Recurrent Neural Network Models for Early Detection of Heart Failure Onset. Journal of the American Medical Informatics Association, 24(2), 361-370. [8]. Esteva, A; Kuprel, B; Novoa, R.A; Ko, J; Swetter, S.M; Blau, H.M & Thrun, S. (2017), Dermatologist-level Classification of Skin Cancer with Deep Neural Networks. Nature, 542(7639), 115-118. [9]. Haque, A; Milstein, A; & Fei-Fei, L. (2020), Illuminating the Dark Spaces of Healthcare with Ambient Intelligence. Nature, 585(7824), 193-202. [10]. Jiang, F; Zheng, Z; Wang, Y. (2018), Artificial Intelligence (AI) in Healthcare: Past, Present and Future. Stroke and Vascular Neurology, 2(4), 230-243. [11]. Lasko, T.A; Denny, J.C & Levy, M.A (2013), Computational Phenotype Discovery Using Unsupervised Feature Learning Over Noisy, Sparse and Irregular Clinical Data. PLoS ONE, 8(6), e66341. [12]. Miotto, R; Li, L; Kidd, B.A & Dudley, J.T (2016), Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Scientific Reports, 6, 26094. [13].Pappachan, B; Ravindrain, P; & Nair, P. (2019), Secure and Scalable Blockchain Framework for Health Monitoring using Wearable Medical Devices. IEEE International Conference on Communication Systems and Networks. [14]. Patel, M.S; Asch, D.A & Volpp, K.G. (2015), Wearable Devices as Facilitators, not Drivers, of Health Behavior Change. JAMA, 313(5), 459-460. [15]. Rajkomar, A; Dean, J. & Kohana, I. (2019), Machine Learning in Medicine. New England Journal of Medicine, 380, 1347-1358. [16]. Rizwan, M; Ghazal, T.M & Raza, B. (2018), Review of Privacy and Security Issues in Internet of Things (IoT) Health Monitoring Systems. IEEE Access, 7, 155531-155547. [17]. Schwab, P; Keller, E & Narayanan, S. (2018), Real-time Anomaly Detection with Latent Correlation Models for ICU Monitoring. IEEE Journal of Biomedical and Health Informatics, 22(3), 708-717. [18]. Topol, E.J; (2019), High-Performance Medicine: The Convergence of Human and Artificial Intelligence. Nature Medicine, 25(1), 44-56. [19]. Wang, F; Casalino, L.P & Khullar, D. (2018), Deep Learning in Medicine- Promise, Progress and Challenges. JAMA Internal Medicine, 178(7), 982-984. [20]. Zhou, X; Lu, Y; Chen, J & Li, Z. (2019), Health Monitoring and Management Using Internet of Things (IoT) Sensing with Cloud-based Processing: Opportunities and Challenges. IEEE International Conference on Services Computing.

Development of a Model for Health Monitoring Using Machine Learning and Data Mining System

Abstract

Keywords

References

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