Abstract

In the field of medicine, has resulted in the need to filter these data to find information that is relevant for specific research problems. However, in the realm of scientific study, the process of selecting the appropriate data or features represents a substantial and challenging problem. Therefore, in this paper, two wrapper feature selection (FS) methods based on novel metaheuristic algorithms named the arithmetic optimization algorithm (AOA) and the great deluge algorithm (GDA) were used to Make an effort to tackle medical diagnostics challenges. The two methods, AOA and AOA-GD were tested on 23 medical benchmark datasets. The hybridization of the GDA with the AOA considerably increased the AOA's search capability, according to all of the experimental data. The AOA-GD method was then compared with two previous wrapper FS approaches, namely, the coronavirus herd immunity optimizer with greedy crossover operator (CHIO-GC); binary moth flame optimization with Lévy flight (LBMFO_V3. When applied to 23 medical benchmark datasets, with an accuracy rate of 0.80, the AOA-GD surpassed the CHIO-GC and LBMFO V3.

Key words: Medical diagnosis, feature selection, arithmetic optimization algorithm, great deluge algorithm