GBWOEM: A Gradient-Based Weight Optimization Model for Improved Predictive Accuracy in Healthcare.

Background: The use of ensemble learning has been crucial for improving predictive accuracy in healthcare, especially with regard to critical diagnostic and classification problems. Ensemble models combine the strengths of multiple ML models and reduce the risk of misclassification, which is important in healthcare, where accurate predictions impact patient outcomes. Methods: This study introduces the Gradient-Based Weight Optimized Ensemble Model (GBWOEM), an advanced ensemble technique that optimizes the weights of five base models: Decision Tree Classifier (DTC), Random Forest Classifier (RFC), Logistic Regression (LR), Multi-Layer Perceptron (MLP), and K-Nearest Neighbours (KNN), through optimizing the weights. Two variants, GBWOEM-R (random weight initialization) and GBWOEM-U (uniform weight initialization), were proposed and tested on five healthcare-related datasets: breast cancer, Pima Indians Diabetes Database, diabetic retinopathy debrecen, obesity level estimation based on physical condition and eating habits, and thyroid diseases. Results: The test accuracy of the proposed models increased to 0.48-8.26% over the traditional ensemble models, such as Adaboost, Catboost, GradientBoost, LightGBM, and XGBoost. Performance metrics, including ROC-AUC analyses, confirmed the model's efficacy in handling imbalanced data, highlighting its potential for advancing predictive consistency in healthcare applications. Conclusion: The GBWOEM model improves the predictive accuracy and offers a reliable solution for healthcare applications even when dealing with the imbalance data. This strategy has the potential to ensure patient outcomes and diagnostic consistency in healthcare settings.