Development of a Student Depression Prediction Model Based on Machine Learning with Algorithm Performance Evaluation
This research explores the implementation of machine learning to predict depression among university students using a dataset of 2.028 responses containing PHQ-9 scores and academic-demographic attributes. The research implements a structured modeling process involving feature selection, normalizati...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Informatics Department, Faculty of Computer Science Bina Darma University
2025-06-01
|
| Series: | Journal of Information Systems and Informatics |
| Subjects: | |
| Online Access: | https://journal-isi.org/index.php/isi/article/view/1087 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This research explores the implementation of machine learning to predict depression among university students using a dataset of 2.028 responses containing PHQ-9 scores and academic-demographic attributes. The research implements a structured modeling process involving feature selection, normalization, the model’s efficacy was gauged through a suite of evaluate measures, encompassing accuracy, precision, recall, F1-score, The support vector machine (SVM) model’s accuracy improved from 58.8% to 99.5% after hyperparameter tuning. This investigation lends itself to the advancement of a proactive identification framework, which hold potential for incorporation within collegiate mental well-being surveillance infrastructures. Future implementations may consider real-time models and expand data sources through digital counseling systems and behavioral analytics |
|---|---|
| ISSN: | 2656-5935 2656-4882 |