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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2049/ http://eprints.intimal.edu.my/2049/1/jods2024_50.pdf http://eprints.intimal.edu.my/2049/2/590 |
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| 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 |
| building | INTI Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T11:58:35Z |
| format | Article |
| id | intimal-2049 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:35Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-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 |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science QA76 Computer software RA Public aspects of medicine T Technology (General) |
| url | http://eprints.intimal.edu.my/2049/ http://eprints.intimal.edu.my/2049/ http://eprints.intimal.edu.my/2049/1/jods2024_50.pdf http://eprints.intimal.edu.my/2049/2/590 |