Explainable machine learning model for predicting spontaneous bacterial peritonitis in cirrhotic patients with ascites.

ABSTRACT: Spontaneous bacterial peritonitis (SBP) is a life-threatening complication in patients with cirrhosis. We aimed to develop an explainable machine learning model to achieve the early prediction and outcome interpretation of SBP. We used CatBoost algorithm to construct MODEL-1 with 46 variables. After dimensionality reduction, we constructed MODEL-2. We calculated and compared the sensitivity and negative predictive value (NPV) of MODEL-1 and MODEL-2. Finally, we used the SHAP (SHapley Additive exPlanations) method to provide insights into the model's outcome or prediction. MODEL-2 (AUROC: 0.822; 95% confidence interval [CI] 0.783-0.856), liked MODEL-1 (AUROC: 0.822; 95% CI 0.784-0.856), could well predict the risk of SBP in cirrhotic ascites patients. The 6 most influential predictive varia

SUBMITTER: Hu Y 

PROVIDER: S-EPMC8569162 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature