Mental Health Monitoring for Undergraduate Students using Neural Network
DOI:
https://doi.org/10.56294/saludcyt20251622Keywords:
Mental health assessment, under – graduate students, Neural Networks, Data Pre – processing, MLP Classifier, Adam Optimizer, Performance MetricsAbstract
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.
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Copyright (c) 2025 Suruchi Dedgaonkar, Priya Shelke, Mohammed Ali Al-Shara, Amol Dhumane, Ankur Goyal, Priti Garad (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
