Machine learning-based classification of diabetes mellitus using sociodemographic, behavioral, and clinical predictor
Researchers have developed machine learning models to improve the prediction of diabetes mellitus in low- and middle-income countries using sociodemographic and clinical data. The study found that ensemble methods, particularly XGBoost, provided the most accurate risk assessments for early intervention.
Why it matters
This research offers a scalable, low-cost diagnostic tool that could significantly improve public health outcomes in resource-constrained regions.
Scientific Reports ( 2026 ) Cite this article
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
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