Why the study?
The well-being of undergraduate students is a growing concern in academia, prompting the use of artificial intelligence and machine learning to track and evaluate their mental health.
Do Neural Networks improve accuracy in tracking and evaluating mental health in undergraduate students compared to other machine learning algorithms?
Do Neural Networks improve accuracy in tracking and evaluating mental health in undergraduate students compared to other machine learning algorithms?
Neural networks demonstrate superior accuracy compared to traditional machine learning algorithms for assessing mental health in undergraduate students based on survey data.
Answers are grounded in this paper and cite their sources.
May support neural network-based mental health screening in undergraduates; leaves open prospective validation before clinical use.
Introduction; In today's academic realm, the well-being of undergraduates is a growing concern. Objective; Our project aims to tackle this by using Artificial Intelligence (AI) and Machine Learning (ML) to track and evaluate students' mental health. Method; We've analysed a dataset from a survey for undergraduates, using a mix of algorithms like SVM, Random Forest, Logistic Regression, and Neural Networks. Result; Our findings show Neural Networks as the standout performer, excelling in accuracy and other metrics. We've detailed our process from data preparation to model training, highlighting Neural Networks' superiority. By optimizing techniques like cross-validation, we've enhanced SVM, Logistic Regression, and Random Forest. Conclusion; This study not only uncovers issues but also proposes a forward-thinking solution, endorsing Neural Networks as a ground-breaking tool for mental health assessment in academia.
No takes yet. Share an insight, caveat, or question.
Dedgaonkar et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: