An Efficient IoT-based Prediction and Diagnosis of Cardiovascular Diseases for Healthcare Using Machine Learning Models

Abstract

The Internet of Things (IoT) and Machine Learning (ML) models are emerging technologies that are changing our daily lives. These are also considered as game-changing technologies in recent years, catalyzing a paradigm change in traditional healthcare practices. Cardiovascular disease (CVD) is considered a major reason for the high death rate around the world. Cardiovascular disease is caused due to several risk factors like an unhealthy diet, sugar, high Blood Pressure (BP) smoking, etc. Preventive treatment and early intervention for those at risk depend heavily on the prompt and accurate prediction of illnesses. Developing prediction models with improved accuracy is essential given the increasing use of electronic health records. Recurrent neural network variations of deep learning are capable of handling sequential time-series data. In remote places often lack access to a skilled cardiologist. Our proposal aims to develop an efficient community-based recommender system using IoT technology to detect and classify heart diseases. To address this issue, machine learning techniques are applied to a dataset to predict patients with cardiovascular disease because it’s difficult for the medical team to identify CVD effectively. A public dataset is used that contains data of 70000 patients gathered at the time of medical examination and each row has 13 attributes. The risk groups were determined by their likelihood of developing cardiovascular disease. As it works successfully in forecasting diseases utilizing the support system.