Analyzing the role of Heart Disease Prediction System using IoT and Machine Learning
Heart disease prediction is very essential in today's environment, various researches has already done to predict heart disease from large dataset. IoT environment basically generate data from different sensors and predict the disease possibility accordingly. Various synthetic data sets content different body parameters which are extracted by specific sensor values, the major role played by machine learning algorithm. In this research we propose heart disease prediction with the combination of IoT and machine learning approach, the IoT environment has established to extract the data from real-time Body Sensor Network (BSN) with intermediate sensing System and store data in the cloud server adequately. Such audit data has considered synthetic information which is basically used to predict heart disease possibility. In this research, we illustrate various machine learning algorithms as well as some deep learning algorithms to achieve drastic supervision for disease prediction. The experimental analysis shows the effectiveness of proposed deep learning classification algorithms over the classical machine learning algorithms.