OP-HHO based feature selection improves the performance of depression classification framework: A gender biased multiband research

Abstract

Depression, as a common yet severe mood disorder, can cause irreversible damage to the brain if not detected and treated in a timely manner. Unfortunately, due to the current limitations of medical and technological conditions, only a small number of patients have been able to receive appropriate treatment. Although the traditional Harris Hawk's Optimization (HHO) algorithm has a strong searching ability for global optima which is helpful of early diagnosis of depression, it is highly prone to getting stuck in local optima during the early iterations. In view of this, the Optimized-Parameter Harris Hawk's Optimization (OP-HHO) algorithm proposed in this study is devised by integrating an exponential decay function. This incorporation endows the algorithm with the capacity to dynamically modulate the search step size, progressively diminish the escape energy, thereby bolstering the local search capabilities and efficaciously circumventing the problem of premature convergence that may stem from overzealous global exploration. The performance of the OP-HHO was tested using 23 benchmark functions. Based on the features selected by the OP-HHO algorithm, depression classification was carried out using the K-Nearest Neighbor (KNN) algorithm in combination with the MODMA database. The accuracy rate reached 96.36% - 97.30% across different brain wave frequencies under happy stimuli, and 100% under sad stimuli. Moreover, the classification results in the overall electroencephalogram (EEG) signals also showed excellent performance. This indicates that the OP-HHO algorithm is highly effective in accurately identifying the key features of depression. Our comparative study conducted reveals the existence of gender differences, which are expected to serve as effective features to further improve the accuracy of depression classification, opening up new avenues for the development of depression diagnosis techniques.