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@article{177129,
        author = {Mr Santosh Shriram Mhaske and Sakshi  Mahadev Ghatol and Vaishnavi Pandurang Sontakke},
        title = {Heart Disease Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {105-109},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177129},
        abstract = {This paper focuses on Heart diseases that are a leading cause of death worldwide, and early detection can significantly improve survival rates. Machine Learning (ML), a form of artificial intelligence, has become a valuable tool in healthcare for analyzing health data and identifying potential signs of disease. In this study, we developed an ML model to predict heart disease using the Cleveland heart disease dataset, applying a feature selection method to reduce the number of features while retaining the most important ones to enhance model performance.
We trained multiple machine learning algorithms and compared their results. The Random Forest classifier outperformed the others, achieving 99.99% Sensitivity, 98.37% Specificity, 98.47% Accuracy, and an Area Under the Curve (AUC) of 94.48%. These results demonstrate that combining effective feature selection with Random Forest can generate highly reliable predictions for heart disease. This approach could assist healthcare professionals in detecting heart disease early and making better clinical decisions},
        keywords = {Heart Disease, Machine Learning, Feature Selection, Cleveland Dataset, Health Prediction},
        month = {April},
        }