According to the Centers for Disease Control (CDC), heart disease is the leading cause of death in most of the country. This research includes Artificial Intelligence (AI) and soft computing techniques to resolve the complexity and performance improvement over conventional methods. The preliminary process is to gather heart data base in real word environment and then to reduce data redundancy and improve data integrity, the data normalization is performed by using Zero-Score (Z-Score). Subsequently, attribute reduction take plays by incorporating soft computing techniques namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Crow Search Optimization (CSO) and Opposition Based Crow Search Optimization (OCSO). Finally the Radial Basis Function-Transductive Support Vector Machines (RBF-TSVM) classifier is used for heart disease prediction. The experimental outcomes display that the proposed OCSO technique attains superior performance related with the existing method in terms of accuracy, sensitivity and specificity
Volume 11 | 06-Special Issue
Pages: 760-769