Decision Support System for Early Detection of Depression Risk Based on Lifestyle Patterns Using the ROC-WASPAS Method
DOI:
https://doi.org/10.30865/ijics.v10i2.9799Keywords:
Decision Support System, Depression Risk, Lifestyle Indicators, Rank Order Centroid, WASPASAbstract
This study develops a decision-support model for prioritizing lifestyle-based depression risk indicators using Rank Order Centroid (ROC) weighting and Weighted Aggregated Sum Product Assessment (WASPAS). The model evaluates five respondents against five criteria: stress level, sleep duration, social interaction, dietary pattern, and physical activity. Criterion priorities were converted into ROC weights, after which the decision matrix was normalized according to cost and benefit orientation. WASPAS then combined the weighted sum and weighted product components using an equal aggregation coefficient. Stress received the largest weight (0.456), followed by sleep duration (0.256), social interaction (0.156), dietary pattern (0.090), and physical activity (0.040). The final preference scores placed A1 first (0.569), followed by A3 (0.557), A2 (0.519), A5 (0.502), and A4 (0.434). These scores represent relative lifestyle profiles within the small study sample; they do not constitute a clinical diagnosis of depression. The model demonstrates a transparent calculation workflow that may support preliminary risk prioritization when its criteria, scales, and weights have been validated by mental-health professionals. Future work should use a larger sample, a validated depression instrument, sensitivity analysis, and external validation before practical screening is considered.
References
[1] B. Levis et al., "Accuracy of the Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: Individual participant data meta-analysis," BMJ, vol. 365, p. l1476, 2019, doi: 10.1136/bmj.l1476.
[2] E. Fonseca-Pedrero et al., "Validation of the Patient Health Questionnaire-9 in adolescents," Psychiatry Research, 2023, doi: 10.1016/j.psychres.2023.115424.
[3] W. Marx et al., "Clinical guidelines for the use of lifestyle-based mental health care in major depressive disorder," World Psychiatry, vol. 22, no. 2, pp. 333-338, 2023, doi: 10.1002/wps.21092.
[4] S. Amiri et al., "The effect of lifestyle interventions on anxiety, depression and stress: A systematic review and meta-analysis," 2024.
[5] M. Berk et al., "Comorbidity between major depressive disorder and physical diseases: A comprehensive review," World Psychiatry, vol. 22, no. 3, pp. 374-400, 2023.
[6] J. Brinsley et al., "Effectiveness of digital lifestyle interventions on symptoms of depression: A systematic review and meta-analysis," 2025.
[7] L. Costantini et al., "Screening for depression in primary care with Patient Health Questionnaire-9: A systematic review," Journal of Affective Disorders, vol. 279, pp. 473-483, 2021, doi: 10.1016/j.jad.2020.09.131.
[8] M. M. Casanovas-Rubio and J. Armengou, "New method for assigning cardinal weights in multi-criteria decision-making," Operational Research, 2024, doi: 10.1007/s12351-024-00833-w.
[9] T. Varshney et al., "Investigation of rank order centroid method for optimal decision making," Scientific Reports, vol. 14, 2024, doi: 10.1038/s41598-024-61945-z.
[10] P. Mic and M. Antmen, "A decision-making model based on TOPSIS, WASPAS, and EDAS methods," SAGE Open, vol. 11, no. 3, 2021, doi: 10.1177/21582440211040115.
[11] F. Barbara et al., "Interactive Internet-based tool proposal for the WASPAS method," Procedia Computer Science, vol. 221, pp. 315-322, 2023, doi: 10.1016/j.procs.2023.07.028.
[12] R. Nuraini, "Decision support system for projector selection using the Weighted Aggregated Sum Product Assessment method," CSRID Journal, vol. 14, no. 3, pp. 228-241, 2022, doi: 10.22303/csrid.14.3.2022.228-241.
[13] T. S. Pratama and A. A. Soebroto, "Sistem pakar untuk deteksi dini tingkat depresi mahasiswa menggunakan Support Vector Machine," Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 6, no. 1, pp. 105-112, 2022.
[14] P. P. P. Sugihartono, N. Hidayat, and T. Tibyani, "Implementasi metode Fuzzy Tsukamoto untuk deteksi dini tingkat depresi mahasiswa," Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 4, no. 10, pp. 3432-3438, 2020.
[15] V. M. M. Siregar and H. Sugara, "Sistem pendukung keputusan pemilihan sepeda motor bekas menggunakan metode WASPAS," Jurnal Teknik Informasi dan Komputer, vol. 5, no. 2, pp. 263-270, 2022, doi: 10.37600/tekinkom.v5i2.393.


