DefinePK

Abstract

Limiting the feature subset size and expanding the classification accuracy for performing the prediction of heart disease among gestational diabetes patients in the dataset is one among the thrust research area in the field of healthcare informatics and its related application domains. In this research work a receptacle artificial bee colony (R-ABC) classifier is employed for performing the aimed task. Certain enhancements are made with the conventional ABC algorithm in terms of using negligible free vitality, swarming separation task conspire, two - point crossover operation and two - way mutation operation are performed. Quick non-commanded arranging are arbitrarily decided for every present solution in the utilized honey bee stage. Between the present solution and its neighbourhood solution and specifically the proposed solution generator is connected to shape another solution set. With the help of edge esteem the classification is performed. Performance metrics such as sensitivity, specificity, true positive rate, false positive rate, precision, accuracy and time taken for feature selection are taken into account. The results are demonstrated with better performance.