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@article{203417,
        author = {Dr. D. Rajeswari and Mrs. K. Sathya and Ms. S. Indra and Dr. G. Jothi},
        title = {Heart Disease Prediction using Kernel based Support Vector Machines},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11961-11966},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203417},
        abstract = {[Font: Heart Disease is the first largest fatality disease all over the global. At the same period, it is curable if it is effectively predicted early. The prediction of heart disease using Kernel based Support Vector Machine (SVM) classifier is proposed in this paper. SVM was trained to classify and predicting the heart disease according to the features presented. Experiments have been conducted of various training-test partitions involving Statlog heart disease datasets from UCI repository.  In the first stage, dimension of heart disease dataset has 13 explanatory variables is reduced to 6 variables using feature selection procedure namely Logistic Regression. In second stage, a support vector machine with different kernel functions namely linear, polynomial, Radial basis and Sigmoid was utilized as classifier. The performance of SVM classifier with each kernel function was evaluated by using performance indices such as accuracy, sensitivity, specificity, and f-measure. The kernel function namely Linear is used to find the best cross validation of training data with parameter (c). Linear kernel function obtained highest accuracy (86.41%) which verifies the efficiency and efficacy of LR-Linear strategy.},
        keywords = {Logistic Regression, Support Vector Machine, Kernel Functions, Classification, Heart Disease},
        month = {May},
        }