An ML-Enabled Internet of Things Framework for Early Detection of Heart Disease.

Autor: Muhammad Y; School of Computer Science and Engineering, Beihang University, Beijing, China., Almoteri M; Department of Management Information Systems, Business Administration College King Saud University, Riyadh 11451, Saudi Arabia., Mujlid H; Department of Computer Engineering, Faculty of Computer Engineering, Taif University, Taif, Saudi Arabia., Alharbi A; Computer Sciences and Information Department, Applied College, Taibah University, Al Madinah Al Munawwarah, Saudi Arabia., Alqurashi F; Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia., Dutta AK; Department of Computer Science and Information Systems, College of Applied Sciences, Al Maarefa University, Riyadh 13713, Saudi Arabia., Almotairi S; Department of Natural and Applied Sciences, Faculty of Community College, Majmaah University, Majmaah, 11952, Saudi Arabia.; Department of Information Systems, Faculty of Computer and Information Sciences, Islamic University of Madinah, 42351, Saudi Arabia., Almohamedh H; Faculty of King Abdulaziz City for Science and Technology (KACST) Riyadh, Riyadh, Saudi Arabia.
Jazyk: angličtina
Zdroj: BioMed research international [Biomed Res Int] 2022 Sep 21; Vol. 2022, pp. 3372296. Date of Electronic Publication: 2022 Sep 21 (Print Publication: 2022).
DOI: 10.1155/2022/3372296
Abstrakt: Healthcare occupies a central role in sustainable societies and has an undeniable impact on the well-being of individuals. However, over the years, various diseases have adversely affected the growth and sustainability of these societies. Among them, heart disease is escalating rapidly in both economically settled and undeveloped nations and leads to fatalities around the globe. To reduce the death ratio caused by this disease, there is a need for a framework to continuously monitor a patient's heart status, essentially doing early detection and prediction of heart disease. This paper proposes a scalable Machine Learning (ML) and Internet of Things-(IoT-) based three-layer architecture to store and process a large amount of clinical data continuously, which is needed for the early detection and monitoring of heart disease. Layer 1 of the proposed framework is used to collect data from IoT wearable/implanted smart sensor nodes, which includes various physiological measures that have significant impact on the deterioration of heart status. Layer 2 stores and processes the patient data on a local web server using various ML classification algorithms. Finally, Layer 3 is used to store the critical data of patients on the cloud. The doctor and other caregivers can access the patient health conditions via an android application, provide services to the patient, and inhibit him/her from further damage. Various performance evaluation measures such as accuracy, sensitivity, specificity, F1-measure, MCC-score, and ROC curve are used to check the efficiency of our proposed IoT-based heart disease prediction framework. It is anticipated that this system will assist the healthcare sector and the doctors in diagnosing heart patients in the initial phases.
Competing Interests: There exists no conflict of Interest.
(Copyright © 2022 Yar Muhammad et al.)
Databáze: MEDLINE
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