Heart Disease Risk Assessment Using Machine Learning

Kiran More; Pranav Upare; Tejas Terdale; Raj Sonune; Pushkar Patankar; Yash Shinde

Heart Disease Risk Assessment Using Machine Learning

Author : Kiran More, Pranav Upare, Tejas Terdale, Raj Sonune, Pushkar Patankar, Yash Shinde Journa Name: International Journal of Science, Engineering and Technology Volume: 14 issue: 3 Year: Volume-14-issue-3 Views : 89

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.