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@article{199948,
        author = {M.Shoaib Ghodimar and Rutika R. Nangare and Tanishka K. Kamble and Pranali P. Lohar},
        title = {Heart Attack Prediction by Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {431-437},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199948},
        abstract = {One of the main causes of death globally, heart disease, makes early and precise forecasting necessary for good prevention and treatment. Using demographic and clinical data, this research suggests a machine learning-based approach to forecasting heart disease. Among other medical factors, the dataset contains crucial health-related characteristics, including age, cholesterol level, and blood pressure. To enhance the data quality and model performance in this study, data preprocessing methods, including dealing with missing values, categorical feature encoding, and feature scaling, were employed. K-Nearest Neighbors (KNN), Decision Tree, and Random Forest, among several supervised machine learning algorithms, are executed and evaluated. Cross-validation approaches are used to evaluate the models to guarantee generalization and dependability. The experimental findings show that ensemble-based techniques, especially random forest, provide more stable and precise predictions than other models. After adjusting the parameters, the optimized KNN model also showed competitive performance. By offering a quick and data-driven approach for the early identification of heart disease, the suggested system can help healthcare professionals. In the future, this might help patients' results and prompt medical care.},
        keywords = {Machine Learning, K Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier, HTML, CSS, Python},
        month = {May},
        }