PEMODELAN DECISION TREE YANG INTERPRETATIF UNTUK KLASIFIKASI TINGKAT DEPRESI PADA MAHASISWA MENGGUNAKAN DATA PHQ-9
Main Article Content
Abstract
This study aims to develop an interpretive Decision Tree model to identify depression levels in college students using Patient Health Questionnaire-9 (PHQ-9) data. Depression in college students is a growing mental health issue, while most machine learning models are still difficult to interpret. Therefore, this study uses Decision Tree as an explainable artificial intelligence (XAI) approach that is able to produce transparent and easy-to-understand decision rules. The research method uses a supervised learning approach with PHQ-9 data represented in text form and converted into numeric using Label Encoding. The target variable is formed based on the depression level category from the total PHQ-9 score. The model was evaluated using a 5-fold cross-validation technique with accuracy, precision, recall, and F1-score metrics. The results showed that the model achieved an accuracy of 54.4% in cross-validation and 68% in training data, with a macro F1-score of 0.67. Feature importance analysis showed that the PHQ2 variable was the most dominant factor in classifying depression. In addition, the Decision Tree structure was able to provide a clear interpretation of the pattern of depressive symptoms in college students. This study shows that Decision Tree has the potential to support early detection of student depression in a more transparent and interpretable manner.
Article Details
References
[2] R. Beiter, et al., The prevalence and correlates of depression, anxiety, and stress in a sample of college students, Journal of Affective Disorders (2015). doi:10.1016/j.jad.2014.10.054
[3] T. M. Evans, et al., Evidence for a mental health crisis in graduate education, Nature Biotechnology (2018). doi:10.1038/nbt.4089.
URL https://doi.org/10.1038/nbt.4089
[4] K. Kroenke, R. L. Spitzer, J. B. Williams, The phq-9: validity of a brief depression severity measure, Journal of General Internal Medicine (2001). doi:10.1046/j.1525-1497.2001.016009606.x.
[5] R. L. Spitzer, K. Kroenke, J. B. W. Williams, Validation and utility of a self-report version of prime-md, JAMA (1999). doi:10.1001/jama. 282.18.1737.
[6] B. Levis, et al., Accuracy of patient health questionnaire-9 (phq-9) for screening to detect major depression, BMJ (2019). doi:10.1136/bmj.I146. URL https://doi.org/10.1136/bmj.l1476
[7] American Psychiatric Association, Diagnostic and statistical manual of mental disorders (dsm-5)Official manual, often via APA or library (2013). doi:10.1176/appi.books.9780890425596.
[8] A. Rajkomar, J. Dean, I. Kohane, Machine learning in medicine, New England Journal of Medicine (2019). doi:10.1056/NEJMra1814259.
URL https://doi.org/10.1056/NEJMra1814259
[9] A. B. R. Shatte, et al., Understanding the effect of adding auto-mated and human coaching to a mobile health physical activity app for afghanistan and iraq veterans: Protocol for a randomized con-trolled trial of the stay strong intervention, JMIR Mental Health (2019). doi:10.2196/12526.
URL https://doi.org/10.2196/12526
[10] E. J. Topol, High-performance medicine: the convergence of human and artificial intelligence, Nature Medicine (2019). doi:10.1038/ s41591-018-0300-7.
URL https://doi.org/10.1038/s41591-018-0300-7
[11] F. Doshi-Velez, B. Kim, Towards a rigorous science of interpretable ml, arXiv (2017). doi:10.48550/arXiv.1702.08608.
URL https://arxiv.org/abs/1702.08608
[12] C. Molnar, Interpretable machine learningOnline book (free) (2020).
URL https://christophm.github.io/interpretable-ml-book/
[13] L. Breiman, J. Friedman, R. A. Olshen, C. J. Stone, Classification and Regression Trees, 1984, classic book, often available via library or archive. doi:10.1201/9781315139470.
URL https://doi.org/10.1201/9781315139470
[14] R. H. Salk, J. S. Hyde, L. Y. Abramson, Gender differences in de-pression in representative national samples: Meta-analyses of diag-noses and symptoms, Psychological Bulletin 143 (8) (2017) 783–822. doi:10.1037/bul0000102. URL https://doi.org/10.1037/bul0000102
[15] J. Dinis, M. Bragança, Quality of sleep and depression in college students: A systematic review, Sleep Science (2018). doi:10.5935/ 1984-0063.20180045. URL https://doi.org/10.5935/1984-0063.20180045
[16] D. Bzdok, A. Meyer-Lindenberg, Machine learning for precision psychia-try, Biological Psychiatry (2018). doi:10.1016/j.bpsc.2017.11.007.
URL https://doi.org/10.1016/j.bpsc.2017.11.007
[17] I. Goodfellow, Y. Bengio, A. Courville, Deep learningMIT Press book (2016). URL https://www.deeplearningbook.org/
[18] S. M. Lundberg, S.-I. Lee, A unified approach to interpreting model predictions, Advances in Neural Information Processing Systems (NeurIPS) (2017). URL https://papers.nips.cc/paper/ 7062-a-unified-approach-to-interpreting-model-predictions
[19] S. M. Lundberg, G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair,
R. Katz, J. Himmelfarb, N. Bansal, S.-I. Lee, From local explanations to global understanding with explainable ai for trees, Nature Machine Intelligence 2 (1) (2020) 56–67. doi:10.1038/s42256-019-0138-9.
URL https://doi.org/10.1038/s42256-019-0138-9
[20] T. Hastie, R. Tibshirani, J. Friedman, The elements of statistical learn-ingSecond edition 2009 (most cited) (2001). URL https://hastie.su.domains/ElemStatLearn/