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@article{197215,
        author = {Yogendra Patel and Samarth R. Parekh},
        title = {Heart Disease Prediction Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6461-6465},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197215},
        abstract = {Being a significant global health challenge, early detection is thus still considered vital in improving patient care with heart disease. This project aims to develop a user- friendly, home-based questionnaire that predicts heart disease, allowing individuals to check their heart status from a distance. We applied diverse machine learning models with more than 400,000 adults from the 2022 CDC survey. The models applied include logistic regression, decision trees, K-Nearest Neighbors (KNN), naive Bayes, random forest, and ensemble techniques, including bagging, boosting, AdaBoost, XGBoost, and a voting classifier, for classification, where performance is checked using classification metrics like precision, recall, and F1 score. The voting classifier that combined the KNN, naive Bayes, AdaBoost, and XGBoost models scored the highest level of performance with a weighted precision of 89.14%, a recall of 85.51%, and an F1 score of 87.01%. These results illustrate the usefulness of the machine learning model in creating an accessible aid for health use to help users identify heart disease quickly through an easily administered survey, thereby enabling timely intervention and better health outcomes.},
        keywords = {Machine Learning (ML), Ensemble Techniques, Voting Classifier, K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Random Forest, Precision, Recall, F1 Score.},
        month = {April},
        }