Heart Disease Risk Assessment Using Machine Learning

Authors: Kiran More, Pranav Upare, Tejas Terdale, Raj Sonune, Pushkar Patankar, Yash Shinde

Abstract: Cardiovascular disease continues to be a major cause of death globally, emphasizing the importance of timely and accurate risk evaluation. This project creates a system based on machine learning to estimate a person’s risk of heart disease by utilizing clinical and lifestyle factors. Numerous supervised algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), Random Forest, and XGBoost—were trained and assessed on a publicly accessible cardiovascular dataset. Following preprocessing and model fine-tuning, performance was evaluated using Accuracy, Precision, Recall, F1-score, and ROC- AUC. Of the models evaluated, Random Forest demonstrated the best predictive capability.

DOI: http://doi.org/10.5281/zenodo.20729166