Sep 2026· Network Modeling Analysis in Health Informatics and Bioinformatics· Vol 15· 0 citations· 39 references
TL;DR
Five-fold cross-validation demonstrated consistent performance across different data partitions, confirming the robustness and stability of the proposed framework, demonstrating strong generalizability and real-world applicability.
A robust and explainable machine learning framework based on a stacked ensemble model that uses Random Forest, Support Vector Machine, and Gradient Boosting as base learners and Catboost as the meta-learner to support more informed and trustworthy decision-making in healthcare applications is presented.
The results underscore the potential of machine learning, particularly the random forest algorithm in aiding early diagnosis of diabetes and assisting healthcare providers to identify high-risk individuals for timely intervention.
Jessie R. Paragas, Kent Claire Apple Joy M. Pallomina, Alexis Luke G. Barlomento· Sigma Journal of Engineering...· 0 citations
Evaluated machine learning algorithms for predicting diabetes risk from routinely available clinical and lifestyle variables confirm that ensemble tree-based methods, particularly Random Forest, provide a reliable, interpretable, and deployable basis for diabetes risk screening, especially in resource-constrained setti...
T. Olayinka· FUDMA Journal of Sciences· 0 citations
The results indicate that the proposed OEMLF can provide an effective combination of predictive accuracy, scalability, computational efficiency, and interpretability for large-scale cardiovascular screening and clinical decision-support applications.
Amar Singh, Yogesh Mohan· International journal of com...· 0 citations
A machine learning-driven framework developed using the healthcare-dataset-stroke-data, comprising diverse clinical attributes associated with cardiovascular conditions, improves both prediction accuracy and model transparency in heart failure classification.
Anton Musthafa, B. C. Krishna· Adolescência e Saúde· 0 citations
Six supervised learning models were developed and compared for diabetes prediction using a dataset and compared for diabetes prediction using a 100k patients records with eight clinical features including gender, age, hypertension, smoking history, heart disease, BMI, HbA1c level, and blood glucose level.
