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

Zifan Yu1, Jiehui Yang2, Jianfeng Tan3

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Machine learning streamlined the Symptom Checklist-90 (SCL-90) from 90 to 29 items, reducing assessment time by 67.78% while maintaining effectiveness. This enhanced psychological assessment tool offers practical mental health evaluation.

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Area of Science:

  • Psychological assessment
  • Machine learning applications
  • Mental health evaluation

Background:

  • The Symptom Checklist-90 (SCL-90) is a widely used psychological assessment tool.
  • Its extensive nature presents challenges in terms of practicality and assessment time.
  • Streamlining the SCL-90 is crucial for broader applicability in diverse settings.

Purpose of the Study:

  • To employ machine learning techniques for simplifying SCL-90 dimensions and items.
  • To validate the accuracy and practicality of a streamlined SCL-90 scale.
  • To reduce assessment time without compromising diagnostic effectiveness.

Main Methods:

  • Utilized Support Vector Classification (SVC) algorithm on 23,028 SCL-90 responses from university students.
  • Reduced the scale from ten dimensions to four, then further simplified items within dimensions.
  • Validated accuracy, sensitivity, specificity, and reliability of the streamlined scale.

Main Results:

  • The SCL-90 was reduced from 90 to 29 items, a 67.78% reduction in item count.
  • The streamlined scale achieved an 89.50% prediction accuracy for the total score using SVC.
  • Individual dimension accuracies exceeded 90%, with sensitivity and specificity above 85%, and reliability coefficients >0.8.

Conclusions:

  • A streamlined SCL-90 (29 items) significantly reduces assessment time while maintaining high reliability (0.95) and effectiveness.
  • The refined scale enables rapid comprehension of mental health status.
  • This optimized version is suitable for widespread application in various assessment contexts.