RESUMEN
Objective:To explore indicators related to visceral fat index by constructing a random forest model.Methods:In this cross-sectional study, the laboratory measures and body composition analysis records of 617 hospital employees (in-service and retired) who underwent physical examination in Heilongjiang Provincial Hospital Health Management Center from March to September 2021 were selected. The subjects were divided into a training set ( n=411) and a test set ( n=206) with the ratio of 2∶1. A total of 110 predictors were included in the model. The model was constructed with the training set and was evaluated with the test set. The optimal number of nodes and decision trees were selected to evaluate the prediction performance of the optimal model. And the top 10 relatively important factors were selected for further investigation. The 617 participants were further divided in to groups according to the visceral fat index: the normal or high visceral fat index group, and the differences of the top 10 relatively important factors were further compared between the two groups. Results:The optimal number of nodes of the final random forest model was 39 and the number of decision trees was 300. The accuracy, precision, sensitivity and specificity of the model was 83.3%, 73.9%, 89.4% and 78.7%, respectively. The area under the receiver operating characteristic curve and 95% confidence interval of the model was 0.881 (0.832-0.931). The top 10 relatively important factors in the model were body mass index, gender, age, serum uric acid, red blood cell count, monocyte cell count, C-peptide, carcinoembryonic antigen, glycosylated hemoglobin and glutamyl transpeptidase. There were significant differences in the up-mentioned 10 indicators between the subjects with normal and high visceral fat index (all P<0.05). Conclusions:The random forest model built in this study has good performance in predicting visceral fat index, and visceral fat is related with changes in liver function, pancreas function and immune function.