IoT-based System for Health Monitoring of Arrhythmia Patients Using Machine Learning Classification Techniques

Abstract

AbstractAn emerging technology Internet of Things (IoT) in wireless technologies and wearable sensors enables effective monitoring of the patients. Using huge volume of data from such health wearable sensors, health condition of in/out patients can be monitored periodically and repeatedly by processing, analyzing, and classifying using machine learning algorithms. This paper proposed an IoT-based system for health monitoring of arrhythmia patient using machine learning classification techniques. The presented method can be used for continuous monitoring of arrhythmia patient using wearable sensors and classifying the data from such sensors by categorizing into various groups using machine learning algorithm. Initially, data sensing is performed using several wearable sensors with the microcontroller; then, using the IOT technology, that sensed data can be transmitted to the cloud storage. After that, the classification of sensed data can be performed using multi-machine learning algorithms with the previous clinical data and dataset. Finally, prediction of arrhythmia can be done based on the classified feature data. From the result analysis, it can be illustrated that the support vector machine (SVM) algorithm provides higher accuracy compared to all other algorithms in predicting the heart disease of arrhythmia. Therefore, this system has proven robust to predict heart disease of arrhythmia.KeywordsWearable sensorsInternet of Things (IoT)Machine learningArrhythmiaHealth monitoring