Predicting Primary Biliary Cholangitis Stages Using Machine Learning with Automated Hyperparameter Optimization and Recursive Feature Elimination
محورهای موضوعی : IT Strategy
Arman Rezasoltani
1
,
Amir Mohammad Khani
2
,
Ali Husseinzadeh Kashan
3
,
Shahram Agah
4
,
Fatemeh Agah
5
1 - Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.
2 - Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.
3 - Department of Industrial Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.
4 - Department of Gastroenterology and Hepatolog, Colorectal Research Center, Iran University of Medical Sciences, Tehran, Iran.
5 - The University of Adelaide, Discipline of Medicine, Adelaide, South Australia, Australia. Fatemeh.
کلید واژه: Primary Biliary Cholangitis, Machine Learning, Recursive Feature Elimination, Optuna, Imbalanced Data.,
چکیده مقاله :
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.
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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