Diagnosis of liver diseases based on artificial intelligence

Abstract

ABSTRACT Due to a series of problems in the diagnosis of liver disease, the mortality rate of liver disease patients is very high. Therefore, it is necessary for doctors and researchers to find a more effective non-invasive diagnostic method to meet clinical needs. We analyzed data from 416 patients with liver disease and 167 patients without liver disease from northeastern Andhra Pradesh, India. On the basis of considering age, gender and other basic data of patients, this paper uses total bilirubin and other clinical data as parameters to build a diagnostic model. In this paper, the accuracy of artificial intelligence method Random Forest (RF) and Support Vector Machine (SVM) model in the diagnosis of liver patients was compared. The results show that the support vector machine model based on Gaussian kernel function is more excellent in diagnostic accuracy, that is, SVM method is more suitable for the diagnosis of liver diseases.