Machine learning approaches for classifying major depressive disorder using biological and neuropsychological markers: A meta-analysis.
Authors
Affiliations (8)
Affiliations (8)
- School of Psychology, Central China Normal University, Wuhan 430079, China; Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China; Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan 430079, China. Electronic address: [email protected].
- School of Psychology, Central China Normal University, Wuhan 430079, China; Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China; Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan 430079, China. Electronic address: [email protected].
- School of Psychology, Central China Normal University, Wuhan 430079, China; Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China; Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan 430079, China. Electronic address: [email protected].
- School of Psychology, Central China Normal University, Wuhan 430079, China; Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China; Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan 430079, China. Electronic address: [email protected].
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA; Klaus Advanced Computing Building, 266 Ferst Drive, Atlanta, Georgia 30332-0765. Electronic address: [email protected].
- Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil; Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) and Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil; Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul, R. Ramiro Barcelos, 2400, Floresta, Porto Alegre, RS 90035002, Brasil. Electronic address: [email protected].
- School of Psychology, Central China Normal University, Wuhan 430079, China; Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China; Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan 430079, China. Electronic address: [email protected].
- School of Psychology, Central China Normal University, Wuhan 430079, China; Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China; Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan 430079, China. Electronic address: [email protected].
Abstract
Traditional diagnostic methods for major depressive disorder (MDD), which rely on subjective assessments, may compromise diagnostic accuracy. In contrast, machine learning models have the potential to classify and diagnose MDD more effectively, reducing the risk of misdiagnosis associated with conventional methods. The aim of this meta-analysis is to evaluate the overall classification accuracy of machine learning models in MDD and examine the effects of machine learning algorithms, biomarkers, diagnostic comparison groups, validation procedures, and participant age on classification performance. As of September 2024, a total of 176 studies were ultimately included in the meta-analysis, encompassing a total of 60,926 participants. A random-effects model was applied to analyze the extracted data, resulting in an overall classification accuracy of 0.825 (95% CI [0.810; 0.839]). Convolutional neural networks significantly outperformed support vector machines (SVM) when using electroencephalography and magnetoencephalography data. Additionally, SVM demonstrated significantly better performance with functional magnetic resonance imaging data compared to graph neural networks and gaussian process classification. The sample size was negatively correlated to classification accuracy. Furthermore, evidence of publication bias was also detected. Therefore, while this study indicates that machine learning models show high accuracy in distinguishing MDD from healthy controls and other psychiatric disorders, further research is required before these findings can be generalized to large-scale clinical practice.