Internet of Things with Machine Learning-Based Smart Cardiovascular Disease Classifier for Healthcare in Secure Platform
Sima Das, J C Das, Subrata Modak, Kaushik Mazumdar
Abstract
Internet of Things (IoT) is a revolutionary technique that creates a new paradigm in traditional healthcare methods. Machine learning is the hazardless technique which is used with IoT systems to assist healthcare professionals, due to which intelligent systems are increasingly being adopted. In this chapter, we focussed on predicting the output of cardiovascular disease using IoT and machine learning methods in secure blockchain platforms. The dataset was collected using electrocardiogram (ECG). ECG is the process in which electrodes are placed on the human chest, and electrical signals from the human heart are collected. After that dataset collection preprocessing was done, 5–15 Hz signals were filtered using a band pass filter and Stationary wavelet transform (SWT), which removed unwanted human motion. Support vector machine (SVM) was used to classify the result by comparing testing and training dataset. The predicted results were classified as the patient suffering from cardiovascular disease or as a healthy person. The whole procedure was done in the data-driven blockchain-based secured platform.