![]() The CVDs include heart attack, stroke, heart failure, arrhythmia, and heart valve problems, whereas asthma, occupational lung diseases, hypoxemia, and hypercapnic are chronic respiratory diseases. Every year, approximately 17.5 million people die due to cardiovascular diseases (CVDs) around 1.59 million people die because of chronic respiratory diseases. Therefore, in the modern era, data- and model-driven intelligent and smart healthcare systems are required to be implemented to not only assist chronic disease patients but also concerned healthcare practitioners and caregivers.Īccording to the World Health Organization (WHO), cardiovascular and chronic respiratory diseases are the major causes of death globally. The prime purpose of initiating ML into medicines is to have reliable medical procedures for patients suffering from different chronic diseases. Among different mechanisms in Computer-aided Medical Applications, Machine Learning (ML) plays a vital role in predicting certain conditions of patients, providing feasibility to doctors and caregivers to render treatment strategies accordingly. Since the past several decades, innovations in technology have greatly benefited multifarious medical applications, especially in the fields of diagnosis, risk assessment, and prognostication. The reliance on medical healthcare systems over technology can never be denied. 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. 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. For caregivers, a 60-second prediction and to facilitate emergency medical assistance, a 3-minute prediction of vital signs is used. 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. In this machine-learning-based prediction and classification model, we have used a real vital sign dataset. Based on the prediction of futuristic values, the proposed system can classify patients' health status to alarm the caregivers and medical experts. 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. 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. ![]() 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. Cardiovascular and chronic respiratory diseases are global threats to public health and cause approximately 19 million deaths worldwide annually. ![]()
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