A Machine Learning-Based Method for Developing the Chinese Symptom Checklist-11 (CSCL-11)
The Chinese version of the Symptom Checklist-90 (SCL-90) is excessively lengthy, resulting in extended completion time and reduced respondent compliance. This study aimed to utilize a condensed subset of items from the Chinese SCL-90 to identify individuals at high risk for psychological disorders b...
| Published in: | Behavioral Sciences |
|---|---|
| Main Authors: | , , , , , , |
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-328X/15/4/459 |
| _version_ | 1849672435235815424 |
|---|---|
| author | Xuanyi Cai Yunan Zhang Meng Su Fan Chang Lei Quan Yixing Liu Bei Wang |
| author_facet | Xuanyi Cai Yunan Zhang Meng Su Fan Chang Lei Quan Yixing Liu Bei Wang |
| author_sort | Xuanyi Cai |
| collection | DOAJ |
| container_title | Behavioral Sciences |
| description | The Chinese version of the Symptom Checklist-90 (SCL-90) is excessively lengthy, resulting in extended completion time and reduced respondent compliance. This study aimed to utilize a condensed subset of items from the Chinese SCL-90 to identify individuals at high risk for psychological disorders based on machine learning methods, forming a concise and efficient preliminary psychopathological screening instrument for the Chinese general population. Analyzing data collected from 4808 SCL-90 psychological surveys, this study applied variable clustering to select the most representative items, resulting in an 11-item scale: the Chinese Symptom Checklist-11 (CSCL-11). The CSCL-11 demonstrated high internal consistency (Cronbach’s α = 0.84). The results of factor analysis supported a single-factor model for the CSCL-11, demonstrating an acceptable fit (SRMR = 0.035, RMSEA = 0.064, CFI = 0.935, and TLI = 0.919). The CSCL-11 demonstrated strong predictive performance for the Global Severity Index (GSI; RMSE = 0.11, R<sup>2</sup> = 0.92, Pearson’s r = 0.96) and various subscale scores (RMSE < 0.25, R<sup>2</sup> > 0.70, Pearson’s r > 0.85). Additionally, it achieved a 96% accuracy rate in identifying individuals at high risk for psychological disorders. The comparison results indicated that the CSCL-11 outperformed SCL-14, SCL-K11, and SCL-K-9 in predicting GSI scores. In identifying high-risk groups, CSCL-11 demonstrated performance similar to that of SCL-14 and surpassed both SCL-K11 and SCL-K-9. The CSCL-11 retains most of the critical information from the original Chinese SCL-90 and serves as a preliminary psychopathological screening tool for the Chinese general population. |
| format | Article |
| id | doaj-art-982fe2175d064f80a97bc8361f287a4e |
| institution | Directory of Open Access Journals |
| issn | 2076-328X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| spelling | doaj-art-982fe2175d064f80a97bc8361f287a4e2025-08-20T02:17:19ZengMDPI AGBehavioral Sciences2076-328X2025-04-0115445910.3390/bs15040459A Machine Learning-Based Method for Developing the Chinese Symptom Checklist-11 (CSCL-11)Xuanyi Cai0Yunan Zhang1Meng Su2Fan Chang3Lei Quan4Yixing Liu5Bei Wang6School of Management, Beijing University of Chinese Medicine, Beijing 102488, ChinaSchool of Life Sciences, Beijing University of Chinese Medicine, Beijing 102488, ChinaUniversity Student Mental Health Education and Counseling Center, Beijing University of Chinese Medicine, Beijing 102488, ChinaUniversity Student Mental Health Education and Counseling Center, Beijing University of Chinese Medicine, Beijing 102488, ChinaSchool of Life Sciences, Beijing University of Chinese Medicine, Beijing 102488, ChinaSchool of Management, Beijing University of Chinese Medicine, Beijing 102488, ChinaSchool of Life Sciences, Beijing University of Chinese Medicine, Beijing 102488, ChinaThe Chinese version of the Symptom Checklist-90 (SCL-90) is excessively lengthy, resulting in extended completion time and reduced respondent compliance. This study aimed to utilize a condensed subset of items from the Chinese SCL-90 to identify individuals at high risk for psychological disorders based on machine learning methods, forming a concise and efficient preliminary psychopathological screening instrument for the Chinese general population. Analyzing data collected from 4808 SCL-90 psychological surveys, this study applied variable clustering to select the most representative items, resulting in an 11-item scale: the Chinese Symptom Checklist-11 (CSCL-11). The CSCL-11 demonstrated high internal consistency (Cronbach’s α = 0.84). The results of factor analysis supported a single-factor model for the CSCL-11, demonstrating an acceptable fit (SRMR = 0.035, RMSEA = 0.064, CFI = 0.935, and TLI = 0.919). The CSCL-11 demonstrated strong predictive performance for the Global Severity Index (GSI; RMSE = 0.11, R<sup>2</sup> = 0.92, Pearson’s r = 0.96) and various subscale scores (RMSE < 0.25, R<sup>2</sup> > 0.70, Pearson’s r > 0.85). Additionally, it achieved a 96% accuracy rate in identifying individuals at high risk for psychological disorders. The comparison results indicated that the CSCL-11 outperformed SCL-14, SCL-K11, and SCL-K-9 in predicting GSI scores. In identifying high-risk groups, CSCL-11 demonstrated performance similar to that of SCL-14 and surpassed both SCL-K11 and SCL-K-9. The CSCL-11 retains most of the critical information from the original Chinese SCL-90 and serves as a preliminary psychopathological screening tool for the Chinese general population.https://www.mdpi.com/2076-328X/15/4/459Chinese version of SCL-90variable clusteringmachine learningscreening instrument |
| spellingShingle | Xuanyi Cai Yunan Zhang Meng Su Fan Chang Lei Quan Yixing Liu Bei Wang A Machine Learning-Based Method for Developing the Chinese Symptom Checklist-11 (CSCL-11) Chinese version of SCL-90 variable clustering machine learning screening instrument |
| title | A Machine Learning-Based Method for Developing the Chinese Symptom Checklist-11 (CSCL-11) |
| title_full | A Machine Learning-Based Method for Developing the Chinese Symptom Checklist-11 (CSCL-11) |
| title_fullStr | A Machine Learning-Based Method for Developing the Chinese Symptom Checklist-11 (CSCL-11) |
| title_full_unstemmed | A Machine Learning-Based Method for Developing the Chinese Symptom Checklist-11 (CSCL-11) |
| title_short | A Machine Learning-Based Method for Developing the Chinese Symptom Checklist-11 (CSCL-11) |
| title_sort | machine learning based method for developing the chinese symptom checklist 11 cscl 11 |
| topic | Chinese version of SCL-90 variable clustering machine learning screening instrument |
| url | https://www.mdpi.com/2076-328X/15/4/459 |
| work_keys_str_mv | AT xuanyicai amachinelearningbasedmethodfordevelopingthechinesesymptomchecklist11cscl11 AT yunanzhang amachinelearningbasedmethodfordevelopingthechinesesymptomchecklist11cscl11 AT mengsu amachinelearningbasedmethodfordevelopingthechinesesymptomchecklist11cscl11 AT fanchang amachinelearningbasedmethodfordevelopingthechinesesymptomchecklist11cscl11 AT leiquan amachinelearningbasedmethodfordevelopingthechinesesymptomchecklist11cscl11 AT yixingliu amachinelearningbasedmethodfordevelopingthechinesesymptomchecklist11cscl11 AT beiwang amachinelearningbasedmethodfordevelopingthechinesesymptomchecklist11cscl11 AT xuanyicai machinelearningbasedmethodfordevelopingthechinesesymptomchecklist11cscl11 AT yunanzhang machinelearningbasedmethodfordevelopingthechinesesymptomchecklist11cscl11 AT mengsu machinelearningbasedmethodfordevelopingthechinesesymptomchecklist11cscl11 AT fanchang machinelearningbasedmethodfordevelopingthechinesesymptomchecklist11cscl11 AT leiquan machinelearningbasedmethodfordevelopingthechinesesymptomchecklist11cscl11 AT yixingliu machinelearningbasedmethodfordevelopingthechinesesymptomchecklist11cscl11 AT beiwang machinelearningbasedmethodfordevelopingthechinesesymptomchecklist11cscl11 |
