Support Vector Regression-based Model to Analyze Prognosis of Infants with Congenital Muscular Torticollis

Abstract

Objectives: Congenital muscular torticollis, a common disorder that refers to the shortening of the sternocleidomastoid in infants, is sensitive to correction through physical therapy when treated early. If physical therapy is unsuccessful, surgery is required. In this study, we developed a support vector regression model for congenital muscular torticollis to investigate the prognosis of the physical therapy treatent in infants. Methods: Fifty-nine infants with congenital muscular torticollis received physical therapy until the degree of neck tilt was less than 5o. After treatment, the mass diameter was reevaluated. Based on the data, a support vector regression model was applied to predict the prognoses. Results: 10-, 20-, and 50-fold cross-tabulation analyses for the proposed model were conducted based on support vector regression and conventional multi-regression method based on least squares. The proposed methodbased on support vector regression was robust and enabled the effective analysis of even a small amount of data containing outliers. Conclusions: The developed support vector regression model is an effective prognostic tool for infants with congenital muscular torticollis who receive physical therapy. Keywords: Support Vector Regression, Mass Diameter, Prediction Model, Torticollis