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<p>We aimed to develop a practical, data-driven, shortened version of the Cancer-related Dysfunctional Beliefs and Attitudes&#xa0;about Sleep (C-DBAS) scale that maintains diagnostic accuracy while minimizing assessment time and efforts for both patients and clinicians. A sample dataset of 564 cancer patients was collected. Responses to 18 items were organized into six groups based on response similarity using exploratory factor analysis and K-means clustering. The most representative item from each group was then selected utilizing eXtreme Gradient Boosting (XGBoost). Subsequently, a symbolic regression-based clinical score generator (SymScore), a newly developed clinical score generator, was employed to assign optimized weights to the selected items, enabling accurate prediction of the total scores for the Dysfunctional Beliefs and Attitudes about Sleep-16 items (DBAS-16) and 2-item Cancer-related Dysfunctional Beliefs about Sleep (C-DBS) questionnaires. Six key items (items 4, 5, 7, 9, and 15 from DBAS-16 and item C2 from C-DBS) were identified, allowing close estimation of the total score for the combined DBAS-16 and C-DBS, referred to as C-DBAS-6. XGBoost applied to C-DBAS-6 demonstrated strong predictive performance, achieving an R<sup>2</sup> value of 0.88 when contrasted with the actual total scores of the combined DBAS-16 and C-DBS. Application of the SymScore to C-DBAS-6 achieved comparable performance with an R<sup>2</sup> value of 0.90, despite being a simpler approach that necessitated only the summation of response weights from a score table without employing a complex machine learning algorithm. The application of&#xa0;SymScore to C-DBAS-6 represents a highly accurate shortened version of the DBAS-16 and C-DBS questionnaires. </p>

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Cancer-related Dysfunctional Beliefs and Attitude about Sleep-6 (C-DBAS-6): a practical and accurate shortened version using XGBoost and SymScore

  • 期刊论文

  • 发表日期：

    2025年6月4日

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  • Olive R. Cawiding,
  • Hyeontae Jo,
  • Saebom Jeon,
  • Faeyza Rishad Ardi,
  • Jae Kyoung Kim,
  • Seockhoon Chung

摘要

We aimed to develop a practical, data-driven, shortened version of the Cancer-related Dysfunctional Beliefs and Attitudes about Sleep (C-DBAS) scale that maintains diagnostic accuracy while minimizing assessment time and efforts for both patients and clinicians. A sample dataset of 564 cancer patients was collected. Responses to 18 items were organized into six groups based on response similarity using exploratory factor analysis and K-means clustering. The most representative item from each group was then selected utilizing eXtreme Gradient Boosting (XGBoost). Subsequently, a symbolic regression-based clinical score generator (SymScore), a newly developed clinical score generator, was employed to assign optimized weights to the selected items, enabling accurate prediction of the total scores for the Dysfunctional Beliefs and Attitudes about Sleep-16 items (DBAS-16) and 2-item Cancer-related Dysfunctional Beliefs about Sleep (C-DBS) questionnaires. Six key items (items 4, 5, 7, 9, and 15 from DBAS-16 and item C2 from C-DBS) were identified, allowing close estimation of the total score for the combined DBAS-16 and C-DBS, referred to as C-DBAS-6. XGBoost applied to C-DBAS-6 demonstrated strong predictive performance, achieving an R2 value of 0.88 when contrasted with the actual total scores of the combined DBAS-16 and C-DBS. Application of the SymScore to C-DBAS-6 achieved comparable performance with an R2 value of 0.90, despite being a simpler approach that necessitated only the summation of response weights from a score table without employing a complex machine learning algorithm. The application of SymScore to C-DBAS-6 represents a highly accurate shortened version of the DBAS-16 and C-DBS questionnaires.