Year
2026
Season
Spring
Paper Type
Master's Thesis
College
College of Arts and Sciences
Degree Name
Master of Science in Mathematical Sciences (MS)
Department
Mathematics & Statistics
NACO controlled Corporate Body
University of North Florida. Department of Mathematics and Statistics
Committee Chairperson
Dr. Fei Heng
Second Advisor
Dr. Elena Buziaianu
Rights Statement
http://rightsstatements.org/vocab/InC/1.0/
Third Advisor
Dr. Ping Sa
Department Chair
Dr. Richard F. Patterson
College Dean
Kaveri Subrahmanyam
Abstract
Liver cirrhosis is associated with substantial morbidity and mortality, making one-year mortality prediction a clinically relevant problem. Using a liver cirrhosis dataset as the motivating application, this thesis evaluates five machine learning classifiers—Logistic Regression, Random Forest, XGBoost, LightGBM, and CatBoost—under five class-imbalance handling strategies: Baseline learning, Random Oversampling, SMOTE-NC, ADASYN, and Cost-Sensitive Learning. Hyperparameter tuning was conducted using randomized search, and predictive performance was assessed over 200 iterations of Monte Carlo Cross-Validation using Accuracy, Precision, Recall, Fl-score, and ROC-AUC.
The results suggest that imbalance-handling strategies can materially affect predictive performance, particularly recall. Because the outcome of interest is death within one year, recall is especially important, as low recall implies failure to identify true high-risk patients. Across the models, baseline learning often achieved high accuracy and, in many cases, high precision, but it generally yielded the weakest recall. In contrast, ADASYN, SMOTE-NC, and Cost-Sensitive Learning generally improved recall, while SMOTE-NC and Cost-Sensitive Learning often produced stronger Fl-scores, indicating a better balance between precision and recall. Overall, the findings suggest that no single imbalance-handling strategy is uniformly optimal across classifiers, but several such strategies can provide a more favorable balance among clinically relevant performance measures in this setting.
Suggested Citation
Trisha, Sumiya Hasan, "A comparative evaluation of data imbalance handling techniques in Machine Learning models for one-year mortality prediction in liver cirrhosis" (2026). UNF Graduate Theses and Dissertations. 1415.
https://digitalcommons.unf.edu/etd/1415
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