An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders

Abstract

Bipolar disorder is marked by mood swings that alternate between mania and depression. The stages of bipolar disorder (BD), as one of the most common mental conditions, are often misdiagnosed as major depressive disorder (MDD), resulting in ineffective treatment and a poor prognosis. As a result, distinguishing MDD from BD at an earlier phase of the disease may aid in more efficient and targeted treatments. In this research, an enhanced ACO (IACO) technique biologically inspired by and following the required ant colony optimization (ACO) was utilized to minimize the number of features by deleting unrelated or redundant feature data. To distinguish MDD and BD individuals, the selected features were loaded into a support vector machine (SVM), a sophisticated mathematical technique for classification process, regression, functional estimates, and modeling operations. In respect of classifications efficiency and frequency of features extracted, the performance of the IACO method was linked to that of regular ACO, particle swarm optimization (PSO), and genetic algorithm (GA) techniques. The validation was performed using a nested cross-validation (CV) approach to produce nearly reliable estimates of classification error.

Survey analysis for optimization algorithms applied to electroencephalogram

“…Alghawli and Taloba [27] has focused on diagnosing and detecting depression, considered one of the most common mental illnesses. The authors have proposed an improved ACO (IACO) technique to reduce the number of features by removing irrelevant or extraneous feature data.…”

“…ACO+RF+CKSVM III dataset Iva, Hand MI and Finger MI 90.85% Alghawli and Taloba [27] IACO+SVM MDD+BD 80.18% GA CHB-MIT Scalp EEG Shon et al [45] GA+KNN DEAP 71.76% Abdi et al [46] MOBCS-KNN standard EEG motor imagery 93.86% Pratiwi et al [47] Hybrid cuckoo research the University of Bonn 90.0 % Yang et al [48] KNN ADLs 94% Mo and Zhao [49] Magnetic bacteria+SVM BCI Competition IV dataset II-a 67%…”