A Novel Ensemble Method for Improving Disease Burden Detection in Imbalanced Epidemiological Data and Large Cancer Registry

Predictive cancer epidemiology applies machine learning to analyze historical medical data for early diagnosis, reduce cancer spread, and identify high-risk individuals. However, data quality issues and class imbalance often hinder these tasks. Existing class imbalance solutions are prone to information loss, weak recognition of the majority class, increased false positives, and reduced reliability. To overcome these drawbacks, we propose a novel enhanced boosting-based method, JEUBoost. First, JEUBoost reduces the majority class size by using Gaussian probability density to estimate sample probabilities and information entropy to measure sample informativeness, creating a more balanced dataset. Second, a modified metaheuristic algorithm, Jaya, is employed to improve probability estimation of high-quality samples by adjusting relevant parameters of the Gaussian model. Third, a customized cost function for Jaya is formulated as an optimization problem to minimize the model’s error rate. Experimental results demonstrate that the performance metrics, including Accuracy, Precision, Recall, F1-score, G-means, and Precision–Recall curves, achieved by JEUBoost range from 68% to 99%. Compared to conventional class imbalance methods, JEUBoost improved Precision by 2.6%, Recall by 5.5%, G-means by 3.2%, and average Precision of the curve by 8.7%, while reducing variance by 82.71%, demonstrating consistent performance gains across all key metrics.