Machine Learning-Based Gallstone Prediction Using Clinical Markers and Multiple Explainable AI Methods

Abstract

Gallstones, also known as cholelithiasis, are hardened deposits of bile (digestive fluid) formed in the gallbladder. Gallstones are a common gastrointestinal condition. They often lead to complications like cholecystitis, biliary obstruction, and pancreatitis. The diagnosis of gallstones involves blood tests, ultrasonography, and Computed Tomography (CT)/Magnetic Resonance Imaging (MRI) scans. Artificial intelligence (AI) and machine learning (ML) have revolutionized medical diagnostics and have led to an increase in the precision and efficiency of diagnosis. In this research study, multiple supervised learning techniques were used to predict the presence of gallstones in patients. The three hyperparameter tuning techniques utilized to optimize predictive performance include: grid search algorithm, randomized search algorithm and Bayesian optimization. The randomized search algorithm performed the best among the three, with an accuracy of 83%. The AdaBoost Classification and the CatBoost Classification techniques were top performers amongst the nine learning techniques utilized in this study. The customized ensemble (stacking) model achieved an F1-score of 84% (95% CI: 71.2–91.8%) and had an AUC of 0.90 using the randomized search algorithm. Further, eight Explainable Artificial Intelligence (XAI) techniques were utilized to interpret the classification results. Vitamin D and C-Reactive Protein (CRP) were identified as the most influential factors in predicting the condition, by XAI techniques. This highlights their significant role in predictive accuracy. The proposed model demonstrates its ability to assist healthcare professionals. It would enable efficient validation of the results obtained from other diagnostic methods.