Leveraging Machine Learning for Early Risk Prediction in Cirrhosis Outcome Patients
Keywords:
Cirrhosis Prediction, Clinical Decision Support, Machine learning, Medical Data, XGBoostAbstract
Millions of individuals worldwide suffer from liver cirrhosis, which is one of the primary causes of mortality. Healthcare professionals may have more opportunities to treat cirrhosis patients effectively if early death prediction is made and it is postulated that death in this cohort would be correlated with laboratory test findings and other relevant diagnoses. In this study five machine learning models, including LR, SVM, XGBoost, AdaBoost and KNN, are implemented and evaluated. The preprocessing steps included feature selection, categorical data encoding, and data balancing using SVMSMOTE. The XGBoost model demonstrated superior performance, achieving 89.55% accuracy, 89.69% precision, 89.55% recall, and an F1-score of 89.59% after balancing. These findings highlight the potential of machine learning models in accurate risk detection in patients with cirrhosis and providing valuable support in clinical decision-making and improving patient treatment.
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Copyright (c) 2025 Yasir Hussein Shakir, Eshaq Aziz Awadh AL Mandhari, Ali Alkhazraji
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