International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Enhancing Mental Health Disorder Detection: A Hybrid Classifier System Using Soft Voting and Ensemble Methods

Author(s) Bhavvya Jain, Harsh Chitaliya, Miral Gopani, Nikki Mehta, Arya Gawde, Sudhir Dhekane
Country India
Abstract Early detection and diagnosis of mental health disorders based on patient-reported symptoms is vital in mental health care. This research proposes an ensemble learning pipeline for predicting mental health disorders using symptoms and demographic data. The pipeline incorporates preprocessing techniques such as standardization, one-hot encoding, and Term Frequency - Inverse Document Frequency transformation, and addresses class imbalance with the Synthetic Minority Over-sampling Technique to enhance predictions of rare disorders. Multiple classifiers, including Random Forest, Gradient Boosting, XGBoost, CatBoost, and LightGBM, are combined in a voting classifier, with each model optimized using Grid Search. The pipeline's performance is evaluated on its ability to predict mental health disorders, showing potential for supporting clinical diagnosis. CatBoost proves effective in certain models, while LightGBM delivers strong performance across most. The models achieve around 95% accuracy, demonstrating strong discrimination between disorders and other conditions.
Keywords Mental Health Disorder Prediction, Voting Classifier, SMOTE, Random Forest, Gradient Boosting, XGBoost, CatBoost, LightGBM, TF-IDF, Ensemble Learning
Field Medical / Pharmacy
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-01-29
Cite This Enhancing Mental Health Disorder Detection: A Hybrid Classifier System Using Soft Voting and Ensemble Methods - Bhavvya Jain, Harsh Chitaliya, Miral Gopani, Nikki Mehta, Arya Gawde, Sudhir Dhekane - IJFMR Volume 7, Issue 1, January-February 2025.
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E-ISSN 2582-2160
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