Liver cirrhosis prediction: The employment of the machine learning-based approaches

• Developed a Quadratic Discriminant Analysis (QDA)-based method for liver cirrhosis prediction, addressing problems associated with intricate class distributions in healthcare data. • Conducted a comparative analysis of 8 ML models (i.e., HistGradientBoosting, Stacking, Gaussian Process) to obtain best performance. • Demonstrated QDA’s best F1-measure and best-balanced precision-recall trade-off out of the ensemble strategies. • Highlighted the importance of stratified data splitting to resolve issues relating to class imbalance and generalization. • Provided actionable recommendations regarding correlations between features (for example, Hepatomegaly-Bilirubin) that impacted QDA’s. Early detection of liver cirrhosis remains problematic due to its asymptomatic onset and the inherent class imbalance in clinical data. This study conducts a comprehensive evaluation of machine learning models for predicting cirrhosis stages, with a focus on addressing these challenges. An approach employing Quadratic Discriminant Analysis (QDA) is benchmarked against seven other models, including powerful ensembles like Stacking and HistGradientBoosting, on a clinical dataset. Methodologies such as SMOTE oversampling, stratified data splitting, and class-specific covariance estimation were implemented to manage data complexity. The results demonstrate that a Stacking ensemble achieves the highest overall predictive performance with a micro-AUC of 0.80. The proposed QDA method also proves to be a highly effective and competitive model, achieving a robust AUC of 0.76 and outperforming several specialized imbalance-learning algorithms. Crucially, QDA offers this strong performance with exceptional computational efficiency. These findings show that while complex ensembles can yield top-tier accuracy, QDA’s capacity to model non-linear feature associations makes it a powerful and practical choice for the diagnosis of cirrhosis.