Cardiovascular Disease Detection Based on Interpretable and Explainable AI

Abstract

This paper presents details of studies conducted to investigate interpretable and explainable machine learning and AI models for cardiovascular disease detection based on the publicly available Cleveland dataset. The study involves evaluating the interpretability and explainability capabilities of tradition shallow machine learning models and their potential for implementation under low resource settings, with limited training data available for model building, as compared to high performing deep learning models, requiring massive training datasets.