Machine Learning in the Service of Mental Health Management: Optimization Metaheuristics for Improving Disorder Classification

Lepa Babic; Stanislava Kozakijevic; Luka Jovanovic; Branislav Radomirovic; Ivana Strumberger; Milos Antonijevic; Ninoslava Jankovic; Smiljana Tedic

doi:10.31181/msa21202522

Authors

Lepa Babic Faculty of Technical Sciences, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia Author https://orcid.org/0009-0009-6536-1652
Stanislava Kozakijevic Faculty Media and Communication, Singidunum University, Karadjordjeva 65, 11000 Belgrade, Serbia Author https://orcid.org/0009-0004-4927-4632
Luka Jovanovic Faculty of Technical Sciences, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia Author https://orcid.org/0000-0001-9402-7391
Branislav Radomirovic Faculty of Technical Sciences, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia Author https://orcid.org/0000-0002-0490-167X
Ivana Strumberger Faculty of Informatics and Computiong, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia Author https://orcid.org/0000-0002-1154-6696
Milos Antonijevic Faculty of Informatics and Computiong, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia Author https://orcid.org/0000-0002-5511-2531
Ninoslava Jankovic Faculty of Technical Sciences, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia Author https://orcid.org/0009-0007-6309-4189
Smiljana Tedic Faculty of Technical Sciences, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia Author https://orcid.org/0009-0004-6168-3682

DOI:

https://doi.org/10.31181/msa21202522

Keywords:

Mental Health Management, AdaBoost, Firefly Algorithm, Hyperparameter Tuning, Hybridization

Abstract

Mental health awareness has become an increasingly pressing societal issue in recent years. Many individuals struggle with undiagnosed disorders, leading to a significantly reduced quality of life. Consequently, timely diagnosis and effective treatment have become essential. Improving mental health care benefits not only individuals but also society as a whole by promoting overall well-being and productivity. However, limited resources and infrastructure often constrain patients' access to mental health professionals. This work seeks to explore the use of advanced machine learning-powered classification algorithms for detecting and identifying mental health disorders with higher accuracy. Since the performance of classification algorithms depends heavily on proper parameter selection, a modified metaheuristic optimization algorithm, based on the firefly algorithm, is introduced to enhance performance and reliability. The proposed approach was evaluated on a publicly available real-world dataset, and a detailed comparative analysis with several contemporary algorithms was conducted. The best-performing models achieved an accuracy exceeding 94%, suggesting the approach's strong viability for real-world assistive applications in mental health care.

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