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Support Vector Regression-based Model to Analyze Prognosis of Infants with Congenital Muscular Torticollis
Suk-Tae Seo1, In-Hee Lee, Chang-Sik Son
1Biomedical Information Technology Center, Keimyung University, Daegu, Korea.
Insights
A new support vector regression model effectively predicts physical therapy outcomes for infants with congenital muscular torticollis (CMT). This tool aids in determining prognosis for early intervention success.
Area of Science:
- Pediatrics
- Rehabilitation Medicine
- Biostatistics
Background:
- Congenital muscular torticollis (CMT) is a common infant disorder involving sternocleidomastoid muscle shortening.
- Early physical therapy is crucial for CMT correction; surgery is an alternative if therapy fails.
Purpose of the Study:
- To develop a support vector regression (SVR) model to predict the prognosis of physical therapy in infants with CMT.
- To evaluate the model's effectiveness in analyzing patient data.
Main Methods:
- Fifty-nine infants with CMT underwent physical therapy until neck tilt was <5°.
- Mass diameter was reevaluated post-treatment.
- An SVR model was applied to predict treatment prognoses using the collected data.
Main Results:
- The SVR model demonstrated robustness in analyzing data, including outliers.
- Cross-tabulation analyses confirmed the model's effectiveness compared to conventional multi-regression.
Conclusions:
- The developed SVR model serves as an effective prognostic tool for infants with CMT undergoing physical therapy.
- This model can assist clinicians in assessing treatment outcomes.
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 5°. 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.
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