November 30, 2023Advances in Multidisciplinary & Scientific Research Journal Publication

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

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Authors

FAFatimah Adamu-FikaKaduna Polytechnic IAIsaiah Ibukunoluwa Adesokan HMHenry Onyeoma Mafua

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Implication

This analysis demonstrates the predictive performance of machine learning models on mental health metrics in university students, suggesting effective interventions.

Key Points

XGBoost achieved 98% accuracy in predicting mental health issues, outperforming other models significantly.
Key metrics such as accuracy, precision, recall, and F1-score were used to assess the models' effectiveness.
The analysis utilized a publicly available dataset from Kaggle to train and evaluate the models' performance.
Findings highlight the potential for scalable, data-driven interventions to support at-risk students effectively.

Cite This Study

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

synapsesocial.com/papers/68af65a1ad7bf08b1eae5b41 https://doi.org/10.22624/aims/accracrossborder2023v2p2p41x

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Implication

This analysis demonstrates the predictive performance of machine learning models on mental health metrics in university students, suggesting effective interventions.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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