Cardiac Clarity: Harnessing Machine Learning for Accurate Heart-Disease Prediction

This project explores the application of machine learning algorithms to detect the presence of heart disease using structured patient data. The aim is to build a classification system that can accurately predict whether a person is likely to have heart disease, based on features such as age, blood pressure, cholesterol levels, and other health indicators. The dataset consists of 1000 entries with 13 key features and one target variable. Four algorithms are implemented: Gaussian Naive Bayes, Support Vector Machine, XGBoost with Explainable AI, and a Stacking Classifier also enhanced with Explainable AI. The integration of explainability provides transparency by highlighting which features contribute most to the prediction. A web interface built with HTML, CSS, JavaScript, and Flask allows users to interact with the system. The model outputs are shown with explanations, supporting interpretability. Overall, the project combines predictive accuracy and model interpretability in a simple and interactive system.

Keywords: Heart disease, Machine learning, Classification, XGBoost, GaussianNB, SVM, Stacking, Explainable AI, Prediction, Flask.