The simplification of the symptom Checklist-90 scale utilizing machine learning techniques

The Symptom Checklist-90 (SCL-90), widely utilized for psychological assessments, faces challenges due to its extensive nature. Streamlining the SCL-90 is essential in order to enhance its practicality without compromising its broad applicability across diverse settings. The objective of this study is to employ machine learning techniques to simplify the dimensions and individual items within each dimension, while simultaneously validating the accuracy and practicality of the streamlined SCL-90 scale. A total of 23,028 valid responses of the SCL-90 were obtained from university students, with positive cases accounting for 49.58 % and negative cases accounting for 50.42 %. The findings demonstrate that by utilizing the Support Vector Classification (SVC) algorithm, it is possible to reduce the scale from ten dimensions to four, achieving an overall prediction accuracy of 89.50 % for the total score. Further simplification of these remaining four dimensions resulted in a reduction from 44 to 29 items per dimension, yielding individual dimension accuracies exceeding 90 %, along with sensitivity and specificity levels surpassing 85 %, and the reliability coefficients consistently exceeded 0.8 across different algorithms. In conclusion, we successfully reduced the number of scale items from 90 to 29, resulting in a reduction of 67.78 % in overall assessment time while maintaining a high reliability coefficient of 0.95. Importantly, the streamlined scale demonstrated no significant decrease in assessment effectiveness. This refined version facilitates rapid comprehension of individuals' comprehensive mental health status and is well-suited for widespread application in experiential settings.