A Random Forest-based Prediction for Liver Cirrhosis

Prediction of cirrhosis through timely screening and diagnosis with effective medical and lifestyle interventions can be taken to delay the disease progression and improve treatment effectiveness. Therefore, early cirrhosis prediction helps reduce associated complications and improve quality of life. This study established a random forest-based algorithm and compared its performance with other machine learning models based on an Indian Liver Patient Records. The dataset was divided 30% of the test set and 70% of the training set. The results showed that the Accuracy, Area under the curve (AUC) and Recall values of Random Forest (RF) model were 0.73, 0.76 and 0.88, respectively, which demonstrated the proposed model can predict the results better than other models. The proposed model for predicting liver cirrhosis in the future will continue to be improved and perfected to improve the accuracy of liver cirrhosis prediction and treatment effect, thereby reducing the health risks and burdens brought by the disease.