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@article{182270,
        author = {Sunaina and Mr. Deepak},
        title = {Study on Heart Disease Prediction Using Machine Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {2811-2816},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182270},
        abstract = {One of the most crucial components of human life is healthcare. One of the worst diseases and a major hindrance to the lives of many individuals worldwide is heart disease. The traditional methods have limitations in that they do not generalize well to new data that is not present in the training set. A significant discrepancy between training and test accuracy points to this. The code performs an exploratory data analysis (EDA), including visualizing the distribution of continuous and categorical features and their relationship with the target variable (heart disease presence). Data preprocessing techniques such as one-hot encoding and Box-Cox transformation are employed to prepare the data for modeling. Three machine learning models – Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) – are trained and evaluated using stratified k-fold cross-validation. Hyper parameter tuning is performed using Grid Search CV to optimize model performance. Model evaluation metrics include precision, recall, F1-score, and accuracy. Results indicate that the Random Forest model achieves the highest recall for the positive class (heart disease), making it the most suitable model for this application. Confusion matrices are visualized for each model to assess their predictive performance. This work demonstrates the effectiveness of machine learning models in heart disease prediction and provides insights into the selection of appropriate models based on desired performance metrics.},
        keywords = {Healthcare system, Heart disease, Machine learning, RF, SVM and KNN.},
        month = {July},
        }