Regression Models of the Ultrasound Score of Madrid Sonographic Enthesis Index in Patients with Spondyloarthritis

Abstract

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Background: Machine learning regression models are built by minimizing the generalization error in the prediction while avoiding overfitting. These techniques enhance the models and forecasts of earlier medical research by achieving greater accuracy than traditional econometric techniques. Objectives: To predict the Madrid Sonographic Enthesis Index (MASEI) in spondyloarthritis (SpA) patients using the best predictor variables, including disease activity and other factors. Methods: There were cross-sectional, descriptive, and observational investigations conducted. We gathered data from 24 SpA patients who received treatment in our clinics from May 2021 to September 2021 and underwent musculoskeletal ultrasonography utilizing the MASEI. Using F-tests and mutual information, we narrowed down the variables to the most important ones. Finally, we used machine learning to estimate a few regression models. Results: The predictor variables with higher values in the feature selection tests explaining the MASEI are activity, Ankylosing Spondylitis Disease Activity Score (ASDAS), corticosteroids, enthesophytes, and male sex. The most accurate regression model was the Support Vector Machine model (R-squared=0.81 in validation). Since this model is a black box, we also computed a classical linear regression (R-Squared=0.60 in validation) because it provides an explicit model given by: MASEI=-4.02+2,75 Enthesofites+4.838 ASDAS. Conclusion: To date, the correlation between MASEI and disease activity in patients with SpA were known. In order to further examine this relationship, we predicted two regressive models to forecast the MASEI index: a classical linear regression model and a Support Vector Machine model with the lowest estimated Root Mean Square Error among 32 alternative machine learning models.