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

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 appropri...

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Main Authors: Noor, Azijah, Silvia, Ratna, M., Muflih, Haldi, Budiman, Usman, Syapotro, Khalisha, Ariyani
Format: Article
Language:English
English
Published: INTI International University 2024
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Online Access:http://eprints.intimal.edu.my/2049/1/jods2024_50.pdf
http://eprints.intimal.edu.my/2049/2/590
http://eprints.intimal.edu.my/2049/
http://ipublishing.intimal.edu.my/jods.html
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spelling my-inti-eprints.20492024-11-26T06:21:40Z http://eprints.intimal.edu.my/2049/ Classification of Mental Health Care Using the ELM, MLP, and CatBoost Stacking Framework Noor, Azijah Silvia, Ratna M., Muflih Haldi, Budiman Usman, Syapotro Khalisha, Ariyani QA75 Electronic computers. Computer science QA76 Computer software RA Public aspects of medicine T Technology (General) 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. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2049/1/jods2024_50.pdf text en cc_by_4 http://eprints.intimal.edu.my/2049/2/590 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 http://ipublishing.intimal.edu.my/jods.html
institution INTI International University
building INTI Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider INTI International University
content_source INTI Institutional Repository
url_provider http://eprints.intimal.edu.my
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
RA Public aspects of medicine
T Technology (General)
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
RA Public aspects of medicine
T Technology (General)
Noor, Azijah
Silvia, Ratna
M., Muflih
Haldi, Budiman
Usman, Syapotro
Khalisha, Ariyani
Classification of Mental Health Care Using the ELM, MLP, and CatBoost Stacking Framework
description 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.
format Article
author Noor, Azijah
Silvia, Ratna
M., Muflih
Haldi, Budiman
Usman, Syapotro
Khalisha, Ariyani
author_facet Noor, Azijah
Silvia, Ratna
M., Muflih
Haldi, Budiman
Usman, Syapotro
Khalisha, Ariyani
author_sort Noor, Azijah
title Classification of Mental Health Care Using the ELM, MLP, and CatBoost Stacking Framework
title_short Classification of Mental Health Care Using the ELM, MLP, and CatBoost Stacking Framework
title_full Classification of Mental Health Care Using the ELM, MLP, and CatBoost Stacking Framework
title_fullStr Classification of Mental Health Care Using the ELM, MLP, and CatBoost Stacking Framework
title_full_unstemmed Classification of Mental Health Care Using the ELM, MLP, and CatBoost Stacking Framework
title_sort classification of mental health care using the elm, mlp, and catboost stacking framework
publisher INTI International University
publishDate 2024
url http://eprints.intimal.edu.my/2049/1/jods2024_50.pdf
http://eprints.intimal.edu.my/2049/2/590
http://eprints.intimal.edu.my/2049/
http://ipublishing.intimal.edu.my/jods.html
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score 13.222552