Explainable Artificial Intelligence (XAI) Approach to Heart Disease Prediction

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Abstract

Cardiovascular continue to stand as the leading cause of mortality globally. The World Health Organization (WHO) noted that in 2019, 32% of all fatalities were attributed to heart-related issues. In India, the Ministry of Health and Family Welfare reported that cardiovascular diseases accounted for 28.1% of the overall deaths. [1] Approximately 85% of individuals diagnosed with heart failure survive the first year after diagnosis, with rates decreasing to around 55% at two years, approximately 33% at five years, and about 35% at ten years. [2] Cardiovascular diseases, in 2019, claimed the lives of approximately 17.9 million people worldwide, making them the primary cause of death, constituting around 32% of all global deaths. [3] Underscoring the critical need for predictive models in early detection, our proposal integrates machine learning with Explainable AI (XAI) and a user interface (UI) tailored for medical professionals to address cardiovascular diseases (CVDs). We have developed a predictive model for heart disease with 96.07% accuracy, integrating factors like peak exercise ST segment slope, maximum heart rate, and exercise-induced angina. Our model prioritizes transparency, offering clear explanations for decisions to build trust in AI-driven healthcare. Emphasizing feature importance, it enables interpretable predictions, facilitating early detection and informed management. Our objective is two-fold: accurate risk identification and providing medical professionals with a user-friendly interface for transparent, reliable decision-making. This research advances healthcare by bridging AI with human understanding, enhancing outcomes in heart disease management.