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Browse normalized, publication-ready research with direct links to its evidence and source.
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Readable research linked to original sources
Browse normalized, publication-ready research with direct links to its evidence and source.
Loading articles data…
Readable research linked to original sources
Browse normalized, publication-ready research with direct links to its evidence and source.
4 articles
Newest firstThis study presents a machine learning framework for the reconstruction of fatigue life and fracture toughness in natural fiber-reinforced composites, evaluating the predictive accuracy of six regression algorithms-Random Forest, Gradient Boosting, Support Vector Machine, Neural Network, Ridge Regression, and Lasso Regression-using a controlled synthetic dataset of 600 samples generated from established Basquin fa…
Open article record in new tab ↗ObjectiveThis study aimed to train and evaluate supervised machine learning walgorithms using electronic health record (EHR) data to accurately estimate gestational age at delivery. Materials and MethodsWe trained random forest, gradient boosting, and ensemble models on EHR data of mother-infant dyads from Vanderbilt University Medical Center(VUMC) and replicated the analyses at University of Michigan (UMich). We…
Esophageal cancer (EC) remains one of the most lethal malignancies worldwide, with poor survival outcomes largely attributable to late-stage diagnosis and limited treatment effectiveness. Early detection and accurate risk stratification are therefore essential for improving clinical management. In this study, we investigate the predictive value of socio-demographic, dietary, behavioral, environmental, and clinical…
BackgroundDistinguishing between non-severe and severe dengue is crucial for timely intervention and reducing morbidity and mortality. Traditional warning signs recommended by the World Health Organization (WHO) offer a practical approach for clinicians but have limitations in sensitivity and specificity. This study evaluates the performance of machine learning (ML) models compared to WHO- recommended warning sign…
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