Construction of a machine learning-based model for stratified assessment of college students’ mental health and design of intervention pathways
19 mar 2025
INFORMAZIONI SU QUESTO ARTICOLO
Pubblicato online: 19 mar 2025
Ricevuto: 07 nov 2024
Accettato: 18 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0476
Parole chiave
© 2025 Jie Gao, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Figure 1.
Figure 2.
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Figure 5.
Mental health assessment index system
| Target layer | Characteristic layer | Basic layer |
|---|---|---|
| Characteristic index system | Personal factor | Life sense |
| Neuroticism | ||
| Quality of sleep | ||
| Self-evaluation | ||
| Health pressure | ||
| Sense of sense | ||
| Life attitude | ||
| favourability | ||
| Extroversion | ||
| Life goal | ||
| Family factor | Family pressure | |
| Relationship pressure | ||
| Career pressure | ||
| Frustration pressure | ||
| School factor | School environmental pressure | |
| Interpersonal pressure | ||
| Academic pressure | ||
| openness |
Confusion matrix
| Actual result | Predictive result | |
|---|---|---|
| 1 (positive) | 0 (non-positive) | |
| 1 (positive) | 54 | 4 |
| 0 (non-positive) | 5 | 102 |
Performance data
| Model | Accuracy rate(%) | Accuracy rate(%) | Recall rate(%) | F1(%) | AUC |
|---|---|---|---|---|---|
| RF | 82.47 | 64.96 | 70.37 | 73.79 | 0.9056 |
| SVM | 83.11 | 79.17 | 70.37 | 74.51 | 0.9067 |
| XGBoost | 85.71 | 80.77 | 77.78 | 79.25 | 0.9148 |
| Model of this article | 90.18 | 92.84 | 92.73 | 94.16 | 0.9154 |