Weighted Soft-Voting Ensembles for Liver Disease Prediction: A Large-Scale Comparative Study with Transparent Evaluation - Ibraigheeth, Mohammad; Odeh, Suhail; Obaid, Mahmoud (Engineering, Technology & Applied Science Research, 2026)
Early detection of liver disease can significantly improve patient outcomes and reduce healthcare costs. This study presents a comparative evaluation of four traditional machine learning classifiers—Logistic Regression, Support Vector Machines, Gaussian Naïve Bayes, and a Multi-Layer Perceptron—alongside an enhanced weighted soft-voting ensemble model. Using a large, publicly available clinical dataset (~30,000 records), a fully nested, leakage-free cross-validation framework is employed to ensure robust and reliable evaluation. The proposed ensemble assigns adaptive weights based on per-fold model performance and demonstrates superior discrimination and calibration compared to individual classifiers. The results highlight the contribution of transparent ensemble modeling in achieving accurate and clinically interpretable liver disease risk detection.