Intelligent Cloud Based Heart Disease Prediction System Empowered with Supervised Machine Learning

Abstract

The innovation in technologies related to health facilities today is increasingly helping to manage patients with different diseases. The most fatal of these is the issue of heart disease that cannot be detected from a naked eye, and attacks as soon as the human exceeds the allowed range of vital signs like pulse rate, body temperature, and blood pressure. The real challenge is to diagnose patients with more diagnostic accuracy and in a timely manner, followed by prescribing appropriate treatments and keeping prescription errors to a minimum. In developing countries, the domain of healthcare is progressing day by day using different Smart healthcare: emerging technologies like cloud computing, fog computing, and mobile computing. Electronic health records (EHRs) are used to manage the huge volume of data using cloud computing. That reduces the storage, processing, and retrieval cost as well as ensuring the availability of data. Machine learning procedures are used to extract hidden patterns and data analytics. In this research, a combination of cloud computing and machine learning algorithm Support vector machine (SVM) is used to predict heart diseases. Simulation results have shown that the proposed intelligent cloud-based heart disease prediction system empowered with a Support vector machine (SVM)-based system model gives 93.33% accuracy, which is better than previously published approaches.