Abstract: This study evaluates the performance of three machine learning models in predicting type 2 diabetes, focusing on their accuracy, sensitivity, and generalization capacity. The methodological ...
Interpreting relatively inexpensive electrocardiograms (ECGs) with an artificial intelligence (AI) algorithm accurately ...
Abstract: Accurate identification and prediction of diabetes complications contribute to improved patient health. However, existing prediction models predominantly employ single-task learning (STL) ...
A new tool named T1GRS enables researchers to get more accurate, further-reaching risk scores for the greater population ...
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