Development of a Berg Balance Scale Short-Form Using a Machine Learning Approach in Patients With Stroke

Background and Purpose: The Berg Balance Scale (BBS) is frequently used in routine clinical care and research settings and has good psychometric properties. This study was conducted to develop a short form of the BBS using a machine learning approach (BBS-ML). Methods: Data of 408 individuals poststroke were extracted from a published database. The initial (ie, 4-, 5-, 6-, 7-, and 8-item) versions were constructed by selecting top-ranked items based on the feature selection algorithm in the artificial neural network model. The final version of the BBS-ML was chosen by selecting the short form that used a smaller number of items to achieve a higher predictive power R-2, a lower 95% limit of agreement (LoA), and an adequate possible scoring point (PSP). An independent sample of 226 persons with stroke was used for external validation. Results: The R-2 values for the initial 4-, 5-, 6-, 7-, and 8-item short forms were 0.93, 0.95, 0.97, 0.97, and 0.97, respectively. The 95% LoAs were 14.2, 12.2, 9.7, 9.6, and 8.9, respectively. The PSPs were 25, 35, 34, 35, and 36, respectively. The 6-item version was selected as the final BBS-ML. Preliminary external validation supported its performance in an independent sample of persons with stroke (R-2 = 0.99, LoA = 10.6, PSP = 37). Discussion and Conclusions: The BBS-ML seems to be a promising short-form alternative to improve administrative efficiency. Future research is needed to examine the psychometric properties and clinical usage of the 6-item BBS-ML in various settings and samples.

Automated summary

This study developed a shortened version of the Berg Balance Scale (BBS-ML) using a machine learning approach to assess the balance ability of stroke patients. The 6-item version was chosen as the final BBS-ML with high predictive power and adequate scoring points. Preliminary external validation supported its performance in an independent sample.