Heart Disease Prediction Using Machine Learning with Explainable AI

Abstract: Heart disease is a major health issue all over the world and therefore it is important for potential risk patterns to be identified early on. Machine Learning (ML) methods are able to examine clinical data and detect patterns that can assist in putting people into one of two categories: those with heart disease and those without heart disease. The present paper describes a heart disease prediction system based on machine learning that makes use of the Cleveland portion of the UCI Heart Disease dataset. The system applies Logistic Regression and Random Forest classifiers to study patient characteristics such as age, sex, chest pain type, resting blood pressure, cholesterol level, maximum heart rate, and other clinical parameters. Before it is split into training and testing sets, the dataset goes through a preprocessing stage. The models are assessed using accuracy, precision, recall, the F1-score, and the Receiver Operating Characteristic Area Under the Curve (ROC-AUC). In order to enhance interpretability, SHAPley Additive explanations (SHAP) are employed to show the contribution of the input features to the model's predictions. A user interface based on Streamlit allows users to input patient information and see the output from the model. The system integrates prediction, model comparison, and explainability within a single application. The actual performance figures are derived from experimentation and have to be stated after the implementation has been carried out. The system is meant for educational and research use only and should not be used as a replacement for professional medical diagnosis.

Keywords: Heart disease prediction, machine learning, Logistic Regression, Random Forest, SHAP, explainable AI, UCI dataset.