Efficient classification of chronic kidney disease by using multi-kernel support vector machine and fruit fly optimization algorithm

In recent days, the gigantic generation of medical data from smart healthcare applications requires the development of big data classification methodologies. Medical data classification can be utilized for visualizing the hidden patterns and finding the presence of disease from the medical data. In this article, we present an efficient multi-kernel support vector machine (MKSVM) and fruit fly optimization algorithm (FFOA) for disease classification. Initially, FFOA is employed to choose the finest features from the available set of features. The selected features from the medical dataset are processed and provided to the MKSVM for medical data classification purposes. The proposed chronic kidney disease (CKD) classification method has been simulated in MATLAB. Next, testing of the dataset takes place using the own benchmark CKD dataset from UCI machine learning repositories such as Kidney chronic, Cleveland, Hungarian, and Switzerland. The performance of the proposed CKD classification method is elected by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, and false negative rate. The investigational outcome specifies that the proposed CKD classification method achieves maximum classification precision value of 98.5% for chronic kidney dataset, 90.42904% for Cleveland, 89.11565% for Hungarian, and 86.17886% for Switzerland dataset than existing hybrid kernel SVM, fuzzy min-max GSO neural network, and SVM methods.