Classification of Mental Health Care Using the ELM, MLP, and CatBoost Stacking Framework

Noor, Azijah and Silvia, Ratna and M., Muflih and Haldi, Budiman and Usman, Syapotro and Khalisha, Ariyani (2024) Classification of Mental Health Care Using the ELM, MLP, and CatBoost Stacking Framework. Journal of Data Science, 2024 (50). pp. 1-6. ISSN 2805-5160

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Abstract

Mental health significantly impacts overall well-being, yet the increasing prevalence of mental health issues presents challenges in their effective classification and treatment. Traditional methods often fail to accurately handle complex, non-linear data, compromising the timeliness and appropriateness of interventions. This study introduces an innovative mental health classification framework, ELM-MLP-CatBoost Stacking, to address these deficiencies. The primary objective is to enhance classification accuracy by integrating three advanced computational techniques: the speed of the Extreme Learning Machine (ELM), the flexibility of the Multi-Layer Perceptron (MLP) for modeling non-linear data, and the predictive refinement of CatBoost as a meta-model. Our methodology involves a stacking approach where ELM and MLP models serve as base learners with CatBoost integrating their outputs to optimize final predictions. Experimental results demonstrate that the ELM-MLP-CatBoost Stacking framework substantially outperforms traditional models, achieving a notable accuracy of 92.76%, an improvement over the MLP’s 92.64% and the ELM’s 69.59%. This framework enhances the reliability and efficiency of mental health condition classifications and paves the way for further research into advanced diagnostic tools. The novelty of this research lies in the synergistic combination of these models, setting a new standard for accuracy and reliability in mental health diagnostics and establishing a robust foundation for future advancements in the field.

Item Type:	Article
Uncontrolled Keywords:	Mental health care classification, ELM-MLP-CatBoost Stacking, Extreme Learning Machine (ELM), Multi-Layer Perceptron (MLP), CatBoost.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > RA Public aspects of medicine T Technology > T Technology (General)
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	26 Nov 2024 06:21
Last Modified:	26 Nov 2024 06:21
URI:	http://eprints.intimal.edu.my/id/eprint/2049

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