Diseases
Prognostic Modeling of 1-Year Survival in Hepatocellular Carcinoma: A Comparative Evaluation of Machine Learning Methods with Class Balancing
By Wang J, Li H, Ju C and 1 others.
Abstract
Objective
To develop and comparatively evaluate machine learning models for predicting 1-year survival in hepatocellular carcinoma (HCC) patients, focusing on the prognostic interplay between chronic liver diseases and diabetes aligned with the EASL Clinical Practice Guidelines (2018), and to assess the impact of class balancing via Synthetic Minority Oversampling Technique (SMOTE) on model performance.
Methods
Retrospective analysis was performed on the UCI HCC Survival Dataset (N = 165). Following median/mode imputation of ~10% missing values, four classifiers were evaluated: Logistic Regression (LR), Multilayer Perceptron (MLP), Gradient Boosting (GB), and Random Forest (RF), each trained with and without SMOTE oversampling (eight variants total). Models were trained on 16 EASL-aligned clinical and biochemical features using a stratified 80/20 train/test split (training N = 132, held-out test N = 33). Performance was evaluated via accuracy, AUC-ROC (with 95% bootstrap confidence intervals across 1000 iterations), precision, recall, and macro F1-score. Stratified 5-fold cross-validation and multiple imputation sensitivity analyses (MissForest, KNN, MICE, complete-case) were conducted. SHAP (SHapley Additive exPlanations) was applied to the top-performing model to provide patient-level interpretability.