Application of machine learning techniques for multi-class classification performance on mental disorders

Ümit Can

doi:10.29132/ijpas.1925035

Application of machine learning techniques for multi-class classification performance on mental disorders

Abstract

The contemporary social framework is predicated on a fiercely competitive system, which amplifies individual pressure and subsequently elevates the incidence of mental diseases, significantly affecting public health. Consequently, governmental and non-governmental groups allocate considerable resources to tackle these concerns. The precise identification of such illnesses via artificial intelligence techniques has become critically significant. These methodologies, widely used across several healthcare sectors, have been increasingly adopted in psychiatry. Evaluating psychiatric data to forecast disease accurately enables doctors to make better decisions and establishes a robust decision-support framework. This work employed prevalent machine learning techniques to identify mental illnesses utilizing a dataset with labels for seven distinct mental states—six disorders and one normative condition. The approaches were initially evaluated on an imbalanced dataset and subsequently on a balanced dataset, facilitating comparison of the outcomes. Among Categorical Boosting, Gradient Boosting, Extreme Gradient Boosting, Support Vector Machine, and Logistic Regression, the Support Vector Machine method demonstrated superior performance on both balanced and imbalanced datasets.

Keywords

References

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Details

Primary Language

English

Subjects

Pattern Recognition

Journal Section

Research Article

Authors

Ümit Can ^*
0000-0002-8832-6317
Türkiye

Publication Date

June 22, 2026

Submission Date

April 7, 2026

Acceptance Date

May 25, 2026

Published in Issue

Year 2026 Volume: 12 Number: 1

DOI

https://doi.org/10.29132/ijpas.1925035

IZ

https://izlik.org/JA89YH27XJ

Cite

RIS / Bibtex

APA

Can, Ü. (2026). Application of machine learning techniques for multi-class classification performance on mental disorders. International Journal of Pure and Applied Sciences, 12(1), 385-405. https://doi.org/10.29132/ijpas.1925035

AMA

1.Can Ü. Application of machine learning techniques for multi-class classification performance on mental disorders. International Journal of Pure and Applied Sciences. 2026;12(1):385-405. doi:10.29132/ijpas.1925035

Chicago

Can, Ümit. 2026. “Application of Machine Learning Techniques for Multi-Class Classification Performance on Mental Disorders”. International Journal of Pure and Applied Sciences 12 (1): 385-405. https://doi.org/10.29132/ijpas.1925035.

EndNote

Can Ü (June 1, 2026) Application of machine learning techniques for multi-class classification performance on mental disorders. International Journal of Pure and Applied Sciences 12 1 385–405.

IEEE

[1]Ü. Can, “Application of machine learning techniques for multi-class classification performance on mental disorders”, International Journal of Pure and Applied Sciences, vol. 12, no. 1, pp. 385–405, June 2026, doi: 10.29132/ijpas.1925035.

ISNAD

Can, Ümit. “Application of Machine Learning Techniques for Multi-Class Classification Performance on Mental Disorders”. International Journal of Pure and Applied Sciences 12/1 (June 1, 2026): 385-405. https://doi.org/10.29132/ijpas.1925035.

JAMA

1.Can Ü. Application of machine learning techniques for multi-class classification performance on mental disorders. International Journal of Pure and Applied Sciences. 2026;12:385–405.

MLA

Can, Ümit. “Application of Machine Learning Techniques for Multi-Class Classification Performance on Mental Disorders”. International Journal of Pure and Applied Sciences, vol. 12, no. 1, June 2026, pp. 385-0, doi:10.29132/ijpas.1925035.

Vancouver

1.Ümit Can. Application of machine learning techniques for multi-class classification performance on mental disorders. International Journal of Pure and Applied Sciences. 2026 Jun. 1;12(1):385-40. doi:10.29132/ijpas.1925035