Evolutionary trajectory hybridization for improving deep learning accuracy in medical data prediction system

Abstract

Medical prediction systems recognize as a significant AI field of study. A huge number of methodologies have been produced in the last decades to solve different kinds of prediction system problems in medical fields, in order to assist physicians in their diagnoses. Deep neural network (DNN) considers an extraordinary method in the medical prediction area. The purpose of this study is to enhance DNN accuracy using a meta-heuristic approach by propose a hybrid model formulated of two meta-heuristic algorithms evolutionary as population-based solution and trajectory as a single-based solution to improve (DNN) to facilitate exploration and exploitation balance and consequently enhance medical dataset prediction. The complex structure of DNN to achieve a best classification accuracy would be useful for meta-heuristic algorithm efficiency for searching the global optimum. To verify the proposal method, the study enhanced DNN with four meta-heuristic algorithms to test the selected medical benchmarks separately. The meta-heuristic algorithms included two population-based algorithms, namely, genetic algorithms GA and differential evolution DE, and two single-based algorithms, namely, simulated annealing SA and Tabu search TS. The proposed methodology registered a significant enhancement in anemia classification comparing with four other meta-heuristic algorithms; the anemia data was composed of a real dataset gathered from Iraqi blood laboratories to detect anemia diseases. Meanwhile, the proposal applied two benchmarks from the University of California repository (UCI), namely, Pima Indian diabetes data and liver disorder diseases. The proposed method attained remarkable results.