A predictive model for the diagnosis of non-alcoholic fatty liver disease based on an integrated machine learning method.

Diagnostic markers for non-alcoholic fatty liver disease (NAFLD) are still needed for screening individuals at risk. In recent years, the machine learning method was used to search for the diagnostic markers of multiple diseases. In this study, we developed and validated a machine learning model to diagnose NAFLD using laboratory indicators. NAFLD patients and non-NAFLD controls were recruited in the training and validation cohorts. The laboratory indicators of the participants in the training cohort were collected, and six indicators including alanine aminotransferase/aspartate aminotransferase (ALT/AST), white blood cells (WBC), alpha-L-fucosidase (AFU), hemoglobin (Hb), triglycerides (TG) and gamma-glutamyl transpeptidase (GGT) were screened out with higher weights by an integrate machine learning method. The areas under the receiver operating characteristic curves (AUROCs) for the selected indicators using logistic regression (LR), random forest (RF) and support vector machine (SVM) were 0.814, 0.837 and 0.810, respectively. Then the binary logistic regression was used to construct the predictive model. What's more, the AUROC of the predicted model was 0.732 in the validation cohort of patients with NAFLD. And the combined AUROC of the six parameters was 0.716 in the mouse model fed with high-fat diet (HFD). In summary, we created a predictive model with six laboratory indicators for the diagnosis of NAFLD based on the machine learning method, which has the potential value for the diagnosis of the NAFLD. AJTR