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@article{174736,
        author = {Mrs T.Seeniselvi and Ms V.HariniPriya and Mr P.Gopinath},
        title = {USING MACHINE LEARNING FOR HEART DISEASE PREDICTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {985-987},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174736},
        abstract = {Heart disease remains a leading cause of mortality worldwide, necessitating effective predictive tools to aid in early diagnosis and prevention. This study explores the application of machine learning (ML) techniques for predicting heart disease using a dataset of 1,200 patient records from the Mohand Amokrane EHS Hospital in Algiers, Algeria. The dataset includes 20 attributes related to patient health, such as age, sex, cholesterol levels, and blood pressure. We employed three prominent ML algorithms Neural Networks, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN)—to develop predictive models. The study involved several key phases: data collection, manual exploration, pre-processing, and modeling. Feature selection was performed using a Pearson correlation matrix, which identified 13 significant attributes for model training. The dataset was then split into training and testing subsets, with 80% allocated for training and 20% for testing. The performance of each algorithm was evaluated across different dataset sizes (600, 800, 1000, and 1200 records). Our results demonstrate that Neural Networks achieved the highest accuracy, consistently outperforming SVM and KNN, with an overall accuracy of 93%. In contrast, SVM and KNN achieved accuracies of 90% and 85.5%, respectively. The stability and superior performance of the Neural Network model make it the most effective choice for heart disease prediction in this study. These findings underscore the potential of machine learning in enhancing early heart disease diagnosis and suggest directions for future research, including the integration of additional data types and advanced algorithms to further improve predictive accuracy.},
        keywords = {Ml, KNN, NN (Neural Networks), SVM},
        month = {April},
        }