Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy

Figure 8 Model results. A: Chi-square automated interaction detection (CHAID) results. The CHAID model had 71.36% accuracy in predicting liver disease. The area under the curve (AUC) and Gini values of this model were 0.746 and 0.493, respectively; B: Classification and regression tree results. The result for the classification and regression tree model showed a better accuracy at predicting the liver disease compared to the CHAID model at 73.24%. The AUC and Gini values of this model were 0.724 and 0.4448, respectively; C: Proposed model results. The model produced an accuracy of 93.65% in predicting liver disease, outperforming the other models significantly. It also recorded a Gini index of 0.97, categorizing it as a highly efficient model in making the distinction between a patient who has liver disease and a patient who is healthy in the given context.