Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Liver Diseases and Con ditions - Non-Alcoholic Fatty Liver Disease is the subject of a report. Accordin g to news reporting from Beijing, People's Republic of China, by NewsRx journali sts, research stated, "Non-alcoholic fatty liver disease (NAFLD) is a common chr onic liver disease, which lacks effective drug treatments. This study aimed to c onstruct an eXtreme Gradient Boosting (XGBoost) prediction model to identify or evaluate potential NAFLD patients." The news correspondents obtained a quote from the research from Capital Medical University, "We conducted a longitudinal study of 22,140 individuals from the Be ijing Health Management Cohort. Variable filtering was performed using the least absolute shrinkage and selection operator. Random Over Sampling Examples was us ed to address imbalanced data. Next, the XGBoost model and the other three machi ne learning (ML) models were built using balanced data. Finally, the variable im portance of the XGBoost model was ranked. Among four ML algorithms, we got that the XGBoost model outperformed the other models with the following results: accu racy of 0.835, sensitivity of 0.835, specificity of 0.834, Youden index of 0.669 , precision of 0.831, recall of 0.835, F-1 score of 0.833, and an area under the curve of 0.914. The top five variables with the greatest impact on the onset of NAFLD were aspartate aminotransferase, cardiometabolic index, body mass index, alanine aminotransferase, and triglyceride-glucose index. The predictive model b ased on the XGBoost algorithm enables early prediction of the onset of NAFLD."
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