Fuzzy-IOWA-Based Group Decision Support System (GDSS) for Interpreting DASS Scores with Dynamic Expert Weighting

Wiharto; Eka  P. Meravigliosi; Umi Salamah; Esti Suryani; Vihi Atina; Pradityo Utomo

doi:10.29207/resti.v10i3.7340

Authors

Wiharto Universitas Sebelas Maret
Eka P. Meravigliosi Universitas Sebelas Maret
Umi Salamah Universitas Sebelas Maret
Esti Suryani Universitas Sebelas Maret
Vihi Atina Universitas Duta Bangsa
Pradityo Utomo Universitas Merdeka Madiun

DOI:

https://doi.org/10.29207/resti.v10i3.7340

Keywords:

DASS, expert weighting, Fuzzy-IOWA, GDSS, mental health

Abstract

This research develops a Group Decision Support System (GDSS) to address subjectivity and ambiguity in the interpretation of Depression Anxiety Stress Scales (DASS-21) scores, particularly in cases of symptom overlap across dimensions. The system introduces a structured weighting mechanism in which the influence of each expert is determined based on objective criteria, including clinical experience, education level, and academic contributions. The methodology applies Simple Additive Weighting (SAW) to quantify the relative importance of five clinical experts, resulting in Expert 2 (27.66%) and Expert 1 (27.36%) having the highest influence within the group. These weights are then incorporated into a Fuzzy-Induced Ordered Weighted Averaging (Fuzzy-IOWA) framework to aggregate expert judgments into a unified consensus model. The results indicate that the proposed approach is able to produce a consistent interpretation structure, with a tendency toward the Anxiety dimension in cases of overlapping symptoms. By integrating expert consensus with patient self-report scores, the system generates a structured interpretation profile of DASS-21 responses. The proposed GDSS provides a systematic and transparent aggregation framework that reflects expert reasoning. However, it is intended as a methodological support tool rather than a substitute for clinical diagnosis. The novelty of this work lies in the integration of SAW-based objective expert capability quantification with Fuzzy-IOWA aggregation and QGDD-based consensus characterization, forming a transparent and mathematically accountable GDSS pipeline that has not been previously applied in the context of DASS-21 interpretation.

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References

P. F. Lovibond and S. H. Lovibond, “The Structure of Negative Emotional States: Comparison of The Depression Anxiety Stress Scales (DASS) With The Beck Depression and Anxiety Inventories,” Behaviour Research and Therapy, vol. 33, no. 3, pp. 335-343, 1995, doi: 10.1016/0005-7967(94)00075-U

E. Wahyuni and W. Cahyawulan, “Factorial Structure of The Depression, Anxiety, Stress Scale (DASS) Among University Students: The Impact of Counseling Services,” in Proc. 8th Int. Conf. Educ. Innov. (ICEI), 2025, pp. 460–474. doi: 10.2991/978-2-38476-360-3_44.

I. P. Clara, B. J. Cox, and M. W. Enns, “Confirmatory Factor Analysis of the Depression-Anxiety-Stress Scales in Depressed and Anxious Patients,” Journal of Psychopathology and Behavioral Assessment, vol. 23, no. 1, pp. 61–67, 2001, doi: 10.1023/A:1011095624717.

J. Zhan, K. Zhang, P. Liu, and W. Pedrycz, “A novel group decision-making approach in multi-scale environments,” Applied Intelligence, vol. 53, no. 12, pp. 15127–15146, Jun. 2023, doi: 10.1007/s10489-022-04279-5.

A. Priya, S. Garg, and N. P. Tigga, “Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms,” Procedia Computer Science, vol. 167, pp. 1258–1267, 2020, doi: 10.1016/j.procs.2020.03.442.

S. R. Marsidi, “Identification of Stress, Anxiety, And Depression Levels of Students In Preparation for The Exit Exam Competency Test,” Journal of Vocational Health Studies, vol. 5, no. 2, pp. 87-93, Nov. 2021, doi: 10.20473/jvhs.v5.i2.2021.87-93.

Shikha Verma, Aman Goyal, and Santushti Gandhi, “Predicting Stress, Anxiety and Depression Among the University Students of India Post-Covid,” Int J Innov Sci Res Technol, vol. 8, no. 8, pp. 2244-2250, 2023, doi: 10.5281/zenodo.8330745

R. T. Sutton, D. Pincock, D. C. Baumgart, D. C. Sadowski, R. N. Fedorak, and K. I. Kroeker, “An overview of clinical decision support systems: benefits, risks, and strategies for success,” npj Digital Medicine. vol. 3, no. pp. 1-10, 2020, doi: 10.1038/s41746-020-0221-y.

M. Salimparsa, K. Sedig, D. J. Lizotte, S. S. Abdullah, N. Chalabianloo, and F. T. Muanda, "Explainable AI for clinical decision support systems: Literature review, key gaps, and research synthesis," Informatics, vol. 12, no. 119, pp. 1–27, 2025, doi:10.3390/informatics12040119.

L. Ali, M. S. Sadi, and O. Goni, “Diagnosis of heart diseases: A fuzzy-logic-based approach,” PLoS One, vol. 19, no. 2, pp. 1-25, Feb. 2024, doi: 10.1371/journal.pone.0293112.

R. R. YAGER, “On Ordered Weighted Averaging Aggregation Operators in Multicriteria Decisionmaking,” in Readings in Fuzzy Sets for Intelligent Systems, D. Dubois, H. Prade, and R. R. Yager, Eds. San Mateo, CA, USA: Morgan Kaufmann, 1993, pp. 80–87, doi: 10.1016/B978-1-4832-1450-4.50011-0.

R. R. Yager, and D. P. Filev, “Induced Ordered Weighted Averaging Operators,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 29, no. 2, pp. 141-150, April 1999, doi: 10.1109/3477.752789.

F. Chiclana, E. Herrera-Viedma, F. Herrera, and S. Alonso, “Some induced ordered weighted averaging operators and their use for solving group decision-making problems based on fuzzy preference relations,” Eur J Oper Res, vol. 182, no. 1, pp. 383–399, Oct. 2007, doi: 10.1016/j.ejor.2006.08.032.

X. Tong and Z. J. Wang, “A group decision framework with intuitionistic preference relations and its application to low carbon supplier selection,” Int J Environ Res Public Health, vol. 13, no. 9, pp. 1-16, Sep. 2016, doi: 10.3390/ijerph13090923.

Wiharto et al., “Model Group Decision Support System Based on Depression Anxiety Stress Scales Using Ordered Weighted Averaging Aggregation Method,” Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 7, no. 2, pp. 307–317, Apr. 2025, doi: 10.35882/jeeemi.v7i2.678.

E. Kipoulas, I. Edwards, R. Radakovic, and P. I. Beazley, “Perceptions of bias and credibility of male and female clinical psychologist and psychiatrist expert witnesses presenting clinical information in the courtroom,” Int J Law Psychiatry, vol. 96, pp. 1-14, Sep. 2024, doi: 10.1016/j.ijlp.2024.102016.

W. Lu, C. Liang, and Y. Ding, A Method Determining the Objective Weights of Experts Based on Evidence Similarity in Group Decision-making,” in Proc. 4th Int. Conf. Wireless Commun., Netw. Mobile Comput., 2008, doi: 10.1109/WiCom.2008.2764.

A. Peña, “The Dreyfus model of clinical problem-solving skills acquisition: a critical perspective.,” Medical Education Online, 2010. doi: 10.3402/meo.v15i0.4846.

M. Younan and K. A. Martire, “Likeability and Expert Persuasion: Dislikeability Reduces the Perceived Persuasiveness of Expert Evidence,” Front Psychol, vol. 12-2021, Dec. 2021, doi: 10.3389/fpsyg.2021.785677.

R. Brammer, “Expertise in Psychological Inquiry: A Computerized Assessment,” ERIC Document ED410261, Mar. 1997. [Online]. Available: https://eric.ed.gov/?id=ED410261 .

M. I. Panjaitan, “Simple Additive Weighting (SAW) method in Determining Beneficiaries of Foundation Benefits,” Login: Jurnal Teknologi Komputer, vol. 13, no. 1, pp. 19–25, Jun. 2019. [Online]. Available: https://media.neliti.com/media/publications/326766-simple-additive-weighting-saw-method-in-f8f093e8.pdf

F. Herrera and E. Herrera-Viedma, “Linguistic decision analysis: steps for solving decision problems under linguistic information,” Fuzzy Sets Syst., vol. 115, no. 1, pp. 67–82, 2000, doi: 10.1016/S0165-0114(99)00024-X.

N. Hussain, H. H. Wang, and C. Buckingham, “Policy based generic autonomic adapter for a context-aware social-collaborative system,” in 2018 International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco, 2018, pp. 1-9, doi: 10.1109/ISACV.2018.8354044.

J. M. Bonsel, A. J. Itiola, A. S. Huberts, G. J. Bonsel, and H. Penton, “The use of patient-reported outcome measures to improve patient-related outcomes - a systematic review,” Health Qual Life Outcomes, vol. 22, no. 1, pp. 1-19 Dec. 2024, doi: 10.1186/s12955-024-02312-4.

S. Al Sharie et al., “Unraveling the Complex Web of Fibromyalgia: A Narrative Review,” Medicina, vol. 60, no. 2, Art. no. 272, 2024, doi: 10.3390/medicina60020272.