Construction of the statistical prediction model for the probability of liver cancer based on interpretable artificial intelligence logic

Current machine learning models under artificial intelligence can only improve prediction accuracy, but their underlying logic remains incomprehensible. Therefore, to provide high prediction accuracy and enhance the interpretability of the model through machine learning, the study selects the Extreme Gradient Boosting (XGBoost) model by comparing multiple models under single learner and integrated learning. Then a cancer probability statistical prediction model is constructed through parameter optimization, and its performance and interpretability are analyzed. The experimental results showed that the Receiver Operating Characteristic (ROC) Area under Curve (AUC) value in the single learner was generally lower than 80%, while the AUC value was 84.4%, surpassing that of the comparison model. Simultaneously, an increase in Alpha-Fetoprotein value greater than 13.5 had a stronger predictive effect when combined with other factors. Smaller serum Alanine Aminotransferase and Alpha-Fetoprotein assay near 0 may produce negative or positive effects, whereas a higher value is more likely to produce a positive effect. This is in line with its clinical significance. Overall, XGBoost effectively improves the out-of-sample prediction accurate and interpretability, which is significant for the actual liver cancer diagnosis prediction.