Analyzing the Relationships of Psychological Symptoms in the SCL-90 Using Genetic Algorithms and Association Rule Mining

Qiang Wang; Daisy S. Yap

doi:10.62051/ijcsit.v3n2.14

Authors

Qiang Wang
Daisy S. Yap

DOI:

https://doi.org/10.62051/ijcsit.v3n2.14

Keywords:

Association Rule, SCL90, Genetic Algorithm, Machine Learning

Abstract

To improve the efficiency of processing SCL-90 data with association rules, this study integrates genetic algorithms with association rule mining techniques to analyze data from the SCL-90. The goal is to identify potential patterns and associations in psychological health issues. The SCL-90 is a widely used tool for assessing psychological symptoms and distress levels, consisting of 90 items that cover multiple dimensions such as obsessive-compulsive symptoms, anxiety, and depression. By employing genetic algorithms for preprocessing and optimizing the SCL-90 data, representative and distinctive feature subsets are selected. Genetic algorithms simulate the processes of natural selection, crossover, and mutation to identify optimal feature combinations, thereby enhancing the efficiency and effectiveness of association rule mining. The selected data is then analyzed using association rule mining techniques to generate rules with high support and confidence, revealing potential links between different psychological symptoms. The combination of genetic algorithms and association rule mining not only improves data processing and pattern discovery efficiency but also provides valuable insights for psychological health assessment and intervention.

Downloads

Download data is not yet available.

References

J. Wu, “Evaluation Model of Product Shape Design Scheme Based on Fuzzy Genetic Algorithm Mining Spatial Association Rules,” Math. Probl. Eng., vol. 2022, pp. 1–10, Mar. 2022, doi: 10.1155/2022/2888472.

S. abd alwahab, “Genetic based method for Mining Association Rules from Text,” 2021, doi: 10.17762/turcomat.v12i9.4795.

B. Xu, S. Ding, and Y. Li, “Data association rules mining method based on genetic optimization algorithm,” J. Phys.: Conf. Ser., vol. 1570, p. 012006, Jun. 2020, doi: 10.1088/1742-6596/1570/1/012006.

X. Liu, “Effect of a Mindfulness-Based Intervention Program on Comprehensive Mental Health Problems of Chinese Undergraduates,” Community Ment Health J, vol. 55, no. 7, pp. 1179–1185, Jun. 2019, doi: 10.1007/s10597-019-00426-4.

S. Casale, S. E. Caplan, and G. Fioravanti, “Positive metacognitions about Internet use: The mediating role in the relationship between emotional dysregulation and problematic use,” Addict. Behav., vol. 59, pp. 84–88, Aug. 2016, doi: 10.1016/j.addbeh.2016.03.014.

M. Li et al., “Psychometric Properties and Measurement Invariance of the Brief Symptom Inventory-18 Among Chinese Insurance Employees,” Front. Psychol., vol. 9, Apr. 2018, doi: 10.3389/fpsyg.2018.00519.

G. H. Franke, S. Jaeger, H. Glaesmer, C. Barkmann, K. Petrowski, and E. Braehler, “Psychometric analysis of the brief symptom inventory 18 (BSI-18) in a representative German sample,” BMC Med Res Methodol, vol. 17, no. 1, Jan. 2017, doi: 10.1186/s12874-016-0283-3.

B. Ghazanfari, F. Afghah, and M. E. Taylor, “Sequential Association Rule Mining for Autonomously Extracting Hierarchical Task Structures in Reinforcement Learning,” IEEE Access, vol. 8, pp. 11782–11799, 2020, doi: 10.1109/access.2020.2965930.

B. Minaei-Bidgoli, R. Barmaki, and M. Nasiri, “Mining numerical association rules via multi-objective genetic algorithms,” Inf. Sci., vol. 233, pp. 15–24, Jun. 2013, doi: 10.1016/j.ins.2013.01.028.

N. Khuda Bux, M. Lu, J. Wang, S. Hussain, and Y. Aljeroudi, “Efficient Association Rules Hiding Using Genetic Algorithms,” Symmetry, vol. 10, no. 11, p. 576, Nov. 2018, doi: 10.3390/sym10110576.

F. Peng, Y. Sun, Z. Chen, and J. Gao, “An Improved Apriori Algorithm for Association Rule Mining in Employability Analysis,” Teh. vjesn., vol. 30, no. 5, Oct. 2023, doi: 10.17559/tv-20230327000481.

V. Javangula, “Mining of High Utility Item sets using Genetic Algorithm,” 2021, doi: 10.17762/turcomat.v12i9.3724.

J. Lei, “Association Rule Mining Algorithm in College Students’ Quality Evaluation System,” J. Electr. Comput. Eng., vol. 2022, pp. 1–9, Apr. 2022, doi: 10.1155/2022/6721504.

U. M. Haque, E. Kabir, and R. Khanam, “Investigating school absenteeism and refusal among Australian children and adolescents using Apriori association rule mining,” Sci Rep, vol. 14, no. 1, Jan. 2024, doi: 10.1038/s41598-024-51230-4.

F. Petry and R. Yager, “Data Mining Using Association Rules for Intuitionistic Fuzzy Data,” Information, vol. 14, no. 7, p. 372, Jun. 2023, doi: 10.3390/info14070372.

R. Agrawal, T. Imieliński, and A. Swami, “Mining association rules between sets of items in large databases,” in Proceedings of the 1993 ACM SIGMOD international conference on Management of data - SIGMOD ’93, New York, New York, USA: ACM Press, 1993, pp. 207–216. doi: 10.1145/170035.170072.

D. Goldberg, “Genetic algorithms in search, optimization, and machine learning,” Choice Rev. Online, vol. 27, no. 02, pp. 27-0936-27–0936, Oct. 1989, doi: 10.5860/choice.27-0936.