ABSTRACT

An Internet of Things (IoT)-based Healthcare program to track and detect serious illnesses is being created to provide better service to the user utilizing online health services. In this work, an effective system for predicting heart illness is developed using the UCI repository data and also healthcare devices due to radioactivity. In addition, classification methods are applied to categorize patient records to detect cardiovascular problems. The classification would be developed utilizing information from the testing set during the training period. During the development stage, individual patients system can track whether or not illness exists. Reference data is used for testing, and a variety of classifications, including J48, Logistic Regression (LR), Multilayer Perception (MLP), and Support Vector Machine (SVM), are used. According to the modeling, the J48 classifications outperform other classifications in terms of effectiveness, clarity; remember F-score, and kappa values. Keywords: IoT, Classifier; Machine learning; cardiovascular problems; Radioactivity; Healthcare
Publication date: 01/11/2021

https://ijbpas.com/pdf/2021/November/MS_IJBPAS_2021_NOV_SPCL1122.pdf

Download PDF
https://doi.org/10.31032/IJBPAS/2021/10.11.1122