An effective feature selection for heart disease prediction with aid of hybrid kernel SVM. (4th July 2019) - Keerthika, T.; Premalatha, K.
In today's modern world cardiovascular disease is the most lethal one. This disease attacks a person so instantly that it hardly gets any time to get treated with. So, diagnosing patients correctly on timely basis is the most challenging task for the medical fraternity. In order to reduce the risk of heart disease, effective feature selection and classification based prediction system is proposed. An efficient feature selection is applied on the high dimensional medical data, for selecting the features fish swarm optimisation algorithm is used. After that, selected features from medical dataset are fed to the HKSVM for classification. The performance of the proposed technique is evaluated by accuracy, sensitivity, specificity, precision, recall and f-measure. Experimental results indicate that the proposed classification framework have outperformed by having better accuracy of 96.03% for Cleveland dataset when compared existing SVM method only achieved 91.41% and optimal rough fuzzy classifier achieved 62.25%.