A Comparative Analysis of Machine Learning Algorithms with Bagging and Boosting for Mental Health Assessment

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 6

Year of Publication : 2024

Authors : Ravita Chahar, Ashutosh Kumar Dubey, Sushil Kumar Narang

10.14445/23488549/IJECE-V11I6P103

How to Cite?

Ravita Chahar, Ashutosh Kumar Dubey, Sushil Kumar Narang, "A Comparative Analysis of Machine Learning Algorithms with Bagging and Boosting for Mental Health Assessment," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 6, pp. 20-39, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I6P103

Abstract:

This study explores machine intelligence algorithms for evaluating mental health conditions on distinct datasets. The primary goal is to identify effective algorithms for anticipating mental health concerns. The central objective is to determine the most productive machine learning algorithms in predicting mental health issues using provided datasets. Various algorithms – Logistic Regression (LR), k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), bagging, and boosting – were employed with diverse parameter settings. The OSMI (Ds1) and the District Mental Health Program dataset (Ds2) were used for experimentation. Among the algorithms tested, LR with an Optimal Threshold (LR-OT) achieved notable performance with accuracy, precision, recall, specificity, and F1-score ranging from 0.87 to 0.89 across both datasets. kNN with Optimized parameters (kNNO) achieved 90% and 91% accuracy rates for Ds1 and Ds2, respectively. SVM obtained average accuracies of 91% and 93% for Ds1 and Ds2, respectively, with specific variations showing superior outcomes. Notably, DT, RF, bagging, and boosting models exhibited R2 scores exceeding 0.70, with bagging and DT yielding the highest R2 score. The study’s findings demonstrate that DT and RF models enhanced by bagging and boosting techniques outperform other algorithms in predicting mental health concerns using the provided datasets. This highlights the significance of employing these models for effective mental health assessment through machine learning.

Keywords:

Machine learning, Logistic regression, k-nearest neighbor, Support vector machine, Decision tree, Random forest.

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