XAI Heart Disease Prediction

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Abstract This project presents a lightweight and interactive web application designed to predict the likelihood of heart disease using clinical parameters. Built using Streamlit, the application leverages a machine learning classification model trained on the widely used Cleveland Heart Disease dataset. To enhance model transparency and trustworthiness, SHAP (SHapley Additive exPlanations) has been integrated, providing users with clear and intuitive visual insights into how each input feature contributes to the prediction. The aim is to create a tool that is not only accurate but also interpretable, helping both developers and potential users understand the logic behind each outcome. The project emphasizes accessibility, explainability, and the practical application of machine learning in the healthcare domain. Keywords- “Heart disease prediction”,”SHAP”,”Streamlit”, ”Machine learning”,” Explainable AI”