Prediction of disease progression of chronic hepatitis C based on XGBoost algorithm

Chronic hepatitis
$C$
is caused by infection with hepatitis
$C$
virus (HCV). Due to the mild symptoms of patients with chronic hepatitis
$C$
, it is difficult to make a clear diagnosis based on clinical manifestations. Using machine learning algorithms to classify and predict the progression of chronic hepatitis
$C$
can be targeted for treatment. This article collects blood data from blood donors and clinical data from patients with chronic hepatitis
$C$
, and uses the XGBoost machine learning algorithm to build a predictive model. The predictive effect of the model is evaluated by three evaluation indicators: accuracy, precision and recall of the model. Experimental results show that the accuracy rate of the XGBoost algorithm prediction model is 91.56%, the accuracy rate is 0.98, and the recall rate is 0.98. Compared with the support vector machine algorithm, k nearest neighbor algorithm, decision tree algorithm, and Adaboost algorithm, this model has better fitting effect and prediction performance, and it is robust. In medicine, it is of great significance for the diagnosis and prediction of the disease progression of patients with chronic hepatitis C.