Predicting Primary Biliary Cholangitis Stages Using Machine Learning with Automated Hyperparameter Optimization and Recursive Feature Elimination

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Rezasoltani Arman; Khani Amir Mohammad; Husseinzadeh Kashan Ali; Agah Shahram; Aga Fatemeh

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Journal Paper

Paper Information

Journal: JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION Year:2025 | Volume:13 | Issue:51 Page(s): 165-176

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Title

Predicting Primary Biliary Cholangitis Stages Using Machine Learning with Automated Hyperparameter Optimization and Recursive Feature Elimination

Author(s)

Keywords

Primary Biliary Cholangitis

Machine Learning

Recursive Feature Elimination

Optuna

Imbalanced Data.

Abstract

This research used modern machine learning ways to predict the stages of primary biliary cholangitis using data from the Mayo Clinic trial. The research aims to obtain high prediction accuracy while representing balanced evaluation metrics. Important techniques include automated hyperparameters optimization with Optuna and Recursive Feature Elimination to improve model performance. Pre-processing included handling missing values, encoding of categorical features, and addressing class imbalances using SMOTE. A total of twelve machine learning algorithms are evaluated with ensemble-based models such as CatBoost and Extra Trees producing much better results. Evaluation metrics take into account all model predictions, including accuracy, precision, recall, F1 score, and ROC-AUC for performing balanced and interpretative evaluations of performances critical for imbalanced datasets. This endeavor includes clinical and laboratory information illustrating the prospect of machine learning in advancing therapeutic diagnosis, emphasizing the rigor and robustness in evaluation laid groundwork for future research to encompass even more generalizable and robust diagnostic tools.

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