Leveraging Machine Learning for Early Risk Prediction in Cirrhosis Outcome Patients

Shakir, Yasir Hussein
Mandhari, Eshaq Aziz Awadh AL
Alkhazraji, Ali

PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Vol. 3 No. 1: PREDATECS July 2025

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.