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@article{170331,
        author = {Dr.G.Aparna and Bandi Shashi Sree Ram Charan Sai and Kolagani Latha and Eluri Surya Vamsi and Korpol Akhil Goud},
        title = {Heart Disease Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {121-127},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170331},
        abstract = {This project investigates the use of various machine learning models for predicting heart disease using a publicly available heart disease dataset. The models selected for this study include the Random Forest Classifier, Logistic Regression, and K-Nearest Neighbors (KNN) Classifier, each chosen for its distinct advantages. The Random Forest model is valued for its robustness and ability to capture non-linear relationships through ensemble learning. Logistic Regression is employed for its simplicity and effectiveness in binary classification tasks, while KNN is used for its non-parametric approach, which excels at capturing proximity-based relationships in the data. The dataset contains 14 features related to patient health metrics, such as age, cholesterol levels, blood pressure, and exercise-induced angina, all of which are utilized to predict the likelihood of heart disease. Performance evaluation metrics, including precision, recall, F1 score, and ROC curves, are used to assess the effectiveness of each model. The results show that combining these models provides complementary insights, offering high accuracy and flexibility in handling both linear and non-linear relationships between features. This project contributes to the growing field of machine learning in healthcare by demonstrating how ensemble methods, linear classifiers, and non-parametric techniques can enhance the early detection of heart disease.},
        keywords = {Heart Disease Prediction, Machine Learning, Random Forest Classifier, Logistic Regression, K-Nearest Neighbors, Classification Models, Ensemble Learning, Healthcare Analytics, Binary Classification, Non-Parametric Learning.},
        month = {December},
        }