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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.<\/jats:p>","DOI":"10.1155\/2022\/1332664","type":"journal-article","created":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T22:50:22Z","timestamp":1656456622000},"page":"1-12","source":"Crossref","is-referenced-by-count":9,"title":["An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6586-2141","authenticated-orcid":true,"given":"Abed","family":"Saif Alghawli","sequence":"first","affiliation":[{"name":"Computer Science Department, Prince Sattam Bin Abdulaziz University, Aflaj, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3558-423X","authenticated-orcid":true,"given":"Ahmed I.","family":"Taloba","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Science and Arts in Qurayyat, Jouf University, Sakaka, Saudi Arabia"},{"name":"Information System Department, Faculty of Computers and Information, Assiut University, Assiut, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-64861-3_23"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.4306\/pi.2014.11.3.243"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1080\/10095020.2022.2068385"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/s0377-2217(99)00357-4"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2015.06.021"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1186\/s13634-015-0251-9"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116355"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2977887"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.11113\/jt.v78.8842"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2021.01.177"},{"key":"11","article-title":"Machine Learning with Electroencephalography Features for Precise Diagnosis of Depression Subtypes","author":"M. 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