A Comparative Analysis of Machine Learning Models for Predicting Mental Health of At-Risk Students

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This research reveals XGBoost's superior predictive performance for mental health in students, highlighting data-driven interventions.

XGBoost achieved 98% accuracy, indicating its effectiveness in predicting mental health issues among students.
Evaluation metrics used include accuracy, precision, recall, and F1-score across four machine learning models.
Using a publicly available dataset from Kaggle, the study assessed various algorithms for mental health prediction.
Findings underscore the necessity of scalable interventions to support at-risk students based on data-driven insights.

Adamu-Fika et al. (2023) studied this question.

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