Using machine learning methods to predict hepatic encephalopathy in cirrhotic patients with unbalanced data

OBJECTIVE: Hepatic encephalopathy (HE) is among the most common complications of cirrhosis. Data for cirrhosis with HE is typically unbalanced. Traditional statistical methods and machine learning algorithms thus cannot identify a few classes. In this paper, we use machine learning algorithms to construct a risk prediction model for liver cirrhosis complicated by HE to improve the efficiency of its prediction. METHOD: We collected medical data from 1,256 patients with cirrhosis and performed preprocessing to extract 81 features from these irregular data. To predict HE in cirrhotic patients, we compared several classification methods: logistic regression, weighted random forest (WRF), SVM, and weighted SVM (WSVM). We also used an additional 722 patients with cirrhosis for external validation of the model. RESULTS: The WRF, WSVM, and logistic regression models exhibited better recognition ability for patients with HE than traditional machine learning models (sensitivity> 0.70), but their ability to identify patients with uncomplicated HE was slightly lower (specificity approximately 85%). The comprehensive evaluation index of the traditional model was higher than those of other models (G-means> 0.80 and F-measure> 0.40). For the WRF, the G-means (0.82), F-measure (0.46), and AUC (0.82) were superior to those of the logistic regression and WSVM models, which means that it can better predict the incidence of HE in patients. CONCLUSION: The WRF model is more suitable for the classification of unbalanced medical data and can be used to construct a risk prediction and evaluation system for liver cirrhosis complicated with HE. The probabilistic prediction models of WRF can help clinicians identify high-risk patients with HE.