Comparision of Different Machine Learning Algorithms to Predict the Diagnostic Accuracy Parameters of Celiac Serological Tests

Abstract

Celiac disease; is an autoimmune digestive system disease characterized by chronic intestinal inflammation and villus antrophy and triggered by dietary gluten genetically susceptible individuals. Diagnosis is based on serological tests and small bowel biopsy. Because of the diversity in the clinical features of the disease, various patient profile and the non-standardized serological tests, it is difficult to diagnose the celiac disease. Sensitivity, specificity, positive and negative predictive values are important parameters for the accuracy of the tests and they are missing in some clinicial studies. It is difficult do standardize the tests with these missing values for clinicians. The aim of this study is to train different machine learning algorithms and to test their performance in prediction of the diagnostic accurary parameters of celiac serological tests. Decision trees are effective machine learning algorithms for predicting potential covariates with %88,7 accuracy.