LIVER DISEASE CLASSIFICATION ANALYSIS USING THE XGBOOST METHOD
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Abstract
Liver disease is a severe pathological condition that can cause liver inflammation due to viral infection, toxic agents, or bacterial invasion, interfering with normal liver function. The death rate from this disease reaches 1.2 million people annually in Southeast Asia and Africa. Liver disease can cause damage to the liver and negatively affect overall body function. To reduce disease progression, it is critical to facilitate early diagnosis, thereby enabling rapid initiation of treatment for affected individuals. Classification methods are widely used to make decisions based on new information from previous data processing through calculation algorithms. This study uses the XGBoost classification method to build a predictive model for liver disease. The results of this study confirm that the XGBoost model is a robust and efficient choice for liver disease classification based on patient data. The use of the XGBoost approach has proven its success in the category of liver disease with an accuracy of up to 95% and an accuracy balance of 95%, demonstrating the effectiveness and efficiency of this method in overcoming class imbalances in liver disease classification data.
Keywords: Xgboost, Liver, Classification, Disease
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