A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases

Cardiovascular and chronic respiratory diseases are global threats to public health and cause approximately 19 million deaths worldwide annually. This high mortality rate can be reduced with the use of technological advancements in medical science that can facilitate continuous monitoring of physiol...

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Autores principales:	Wajid Shah, Muhammad Aleem, Muhammad Azhar Iqbal, Muhammad Arshad Islam, Usman Ahmed, Gautam Srivastava, Jerry Chun-Wei Lin
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine (General) R5-920 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/2222d63ddc1a4d15b255091dffaa865a
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spelling	oai:doaj.org-article:2222d63ddc1a4d15b255091dffaa865a2021-11-15T01:19:12ZA Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases2040-230910.1155/2021/2621655https://doaj.org/article/2222d63ddc1a4d15b255091dffaa865a2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2621655https://doaj.org/toc/2040-2309Cardiovascular and chronic respiratory diseases are global threats to public health and cause approximately 19 million deaths worldwide annually. This high mortality rate can be reduced with the use of technological advancements in medical science that can facilitate continuous monitoring of physiological parameters—blood pressure, cholesterol levels, blood glucose, etc. The futuristic values of these critical physiological or vital sign parameters not only enable in-time assistance from medical experts and caregivers but also help patients manage their health status by receiving relevant regular alerts/advice from healthcare practitioners. In this study, we propose a machine-learning-based prediction and classification system to determine futuristic values of related vital signs for both cardiovascular and chronic respiratory diseases. Based on the prediction of futuristic values, the proposed system can classify patients’ health status to alarm the caregivers and medical experts. In this machine-learning-based prediction and classification model, we have used a real vital sign dataset. To predict the next 1–3 minutes of vital sign values, several regression techniques (i.e., linear regression and polynomial regression of degrees 2, 3, and 4) have been tested. For caregivers, a 60-second prediction and to facilitate emergency medical assistance, a 3-minute prediction of vital signs is used. Based on the predicted vital signs values, the patient’s overall health is assessed using three machine learning classifiers, i.e., Support Vector Machine (SVM), Naive Bayes, and Decision Tree. Our results show that the Decision Tree can correctly classify a patient’s health status based on abnormal vital sign values and is helpful in timely medical care to the patients.Wajid ShahMuhammad AleemMuhammad Azhar IqbalMuhammad Arshad IslamUsman AhmedGautam SrivastavaJerry Chun-Wei LinHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920 Medical technology R855-855.5
spellingShingle	Medicine (General) R5-920 Medical technology R855-855.5 Wajid Shah Muhammad Aleem Muhammad Azhar Iqbal Muhammad Arshad Islam Usman Ahmed Gautam Srivastava Jerry Chun-Wei Lin A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases
description	Cardiovascular and chronic respiratory diseases are global threats to public health and cause approximately 19 million deaths worldwide annually. This high mortality rate can be reduced with the use of technological advancements in medical science that can facilitate continuous monitoring of physiological parameters—blood pressure, cholesterol levels, blood glucose, etc. The futuristic values of these critical physiological or vital sign parameters not only enable in-time assistance from medical experts and caregivers but also help patients manage their health status by receiving relevant regular alerts/advice from healthcare practitioners. In this study, we propose a machine-learning-based prediction and classification system to determine futuristic values of related vital signs for both cardiovascular and chronic respiratory diseases. Based on the prediction of futuristic values, the proposed system can classify patients’ health status to alarm the caregivers and medical experts. In this machine-learning-based prediction and classification model, we have used a real vital sign dataset. To predict the next 1–3 minutes of vital sign values, several regression techniques (i.e., linear regression and polynomial regression of degrees 2, 3, and 4) have been tested. For caregivers, a 60-second prediction and to facilitate emergency medical assistance, a 3-minute prediction of vital signs is used. Based on the predicted vital signs values, the patient’s overall health is assessed using three machine learning classifiers, i.e., Support Vector Machine (SVM), Naive Bayes, and Decision Tree. Our results show that the Decision Tree can correctly classify a patient’s health status based on abnormal vital sign values and is helpful in timely medical care to the patients.
format	article
author	Wajid Shah Muhammad Aleem Muhammad Azhar Iqbal Muhammad Arshad Islam Usman Ahmed Gautam Srivastava Jerry Chun-Wei Lin
author_facet	Wajid Shah Muhammad Aleem Muhammad Azhar Iqbal Muhammad Arshad Islam Usman Ahmed Gautam Srivastava Jerry Chun-Wei Lin
author_sort	Wajid Shah
title	A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases
title_short	A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases
title_full	A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases
title_fullStr	A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases
title_full_unstemmed	A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases
title_sort	machine-learning-based system for prediction of cardiovascular and chronic respiratory diseases
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/2222d63ddc1a4d15b255091dffaa865a
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A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases

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