A Secure Remote Health Monitoring for Heart Disease Prediction Using Machine Learning and Deep Learning Techniques in Explainable Artificial Intelligence Framework

Abstract

Cardiovascular diseases (CVD) are the most prevalent cause of death worldwide and have become an important concern for the physicians. Clinical practices have often failed to achieve high accuracy in CVD prediction. Machine learning provides benefits not only for clinical prediction but also for feature ranking, which improves clinical professionals’ interpretation of outputs. The explainable artificial intelligence (XAI) concept seeks to address the lack of explainability in machine learning and deep learning models and provides healthcare professionals with patient-tailored decision-making tools for improving treatments and diagnostics. This paper aims to predict heart disease using a RHMIoT model in the XAI framework.

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