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...
保存先:
| 主要な著者: | , , , , , |
|---|---|
| フォーマット: | 論文 |
| 言語: | English English |
| 出版事項: |
INTI International University
2024
|
| 主題: | |
| オンライン･アクセス: | 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 |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|
| 要約: | 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. |
|---|