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@article{203453,
        author = {Narendar.E and Arthi Priyadharshini and Ponkishore.S and Shiraj.M},
        title = {Heart Disease Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11738-11744},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203453},
        abstract = {heart disease is one of the major causes of death worldwide, and early prediction can help reduce mortality rates through timely medical intervention. This project presents a machine learning-based heart disease prediction system that analyzes patient health parameters to predict the likelihood of heart disease. The dataset used for this study contains medical attributes such as age, blood pressure, cholesterol level, heart rate, chest pain type, and other clinical factors. Various machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) were implemented and evaluated to identify the most accurate prediction model. Data preprocessing techniques such as handling missing values, normalization, and feature selection were applied to improve model performance. The models were trained and tested using standard evaluation metrics including accuracy, precision, recall, F1-score, and confusion matrix. Among the implemented models, the Random Forest algorithm achieved the highest prediction accuracy, demonstrating its effectiveness in heart disease prediction.},
        keywords = {Heart Disease Prediction, Machine Learning, Healthcare Analytics, Early Disease Detection, Clinical Data Analysis},
        month = {May},
        }