Article

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

2025-10-24

Abstract excerpt

<h4>Background: </h4> 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 Gra...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 30548738-3b3f-5940-a130-8f144dd3f01c
DOI: 10.12688/f1000research.169436.1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

GBWOEM: A Gradient-Based Weight Optimization Model for Improved Predictive Accuracy in HealthcareDOI 10.12688/f1000research.169436.1Read

GBWOEM: A Gradient-Based Weight Optimization Model for Improved Predictive Accuracy in HealthcareSemantic proximity Read

Multi-Dataset Diabetes Prediction Using Stacking Ensemble Learning and Explainable Artificial IntelligenceSemantic proximity Read

An Explainable Stacking Ensemble with Cost-Sensitive Threshold Optimization for Gestational Diabetes Risk PredictionSemantic proximity Read

An Explainable Maximum Expected Utility-Optimized Ensemble for Early Prediction of Gestational Diabetes MellitusSemantic proximity Read

Optimizing Machine Learning for Diabetes Detection: Addressing Class Imbalance with SMOTE and Random Forest Ensemble LearningSemantic proximity Read

Development of Explainable Machine Learning Framework for Early Detection and Risk Stratification of Diabetes in Age Specific VariationsSemantic proximity Read

WITHDRAWN: A Machine Learning Model for Real-Time Hypoglycemia Risk Prediction in Hospitalized Diabetic Patients: Development and ValidationSemantic proximity Read

Benchmarking Ensemble Machine Learning Algorithms for the Early Prediction of Stroke in Imbalanced Clinical Cohorts: A Comparative Analysis and Decision Curve AssessmentSemantic proximity Read

Enhancing Prediction of Heart Disease Using Hybrid Machine Learning Methods XGBoost and K-Means ClusteringSemantic proximity Read

Towards Reliable Diabetes Prediction: Innovations in Data Engineering and Machine Learning ApplicationsSemantic proximity Read

Comparative Efficacy of Statistical Learning and Ensemble Methods for Multivariate Obesity Condition ClassificationSemantic proximity Read

The Impact of the SMOTE Method on Machine Learning and Ensemble Learning Performance Results in Addressing Class Imbalance in Data Used for Predicting Total Testosterone Deficiency in Type 2 Diabetes PatientsSemantic proximity Read