https://www.selleckchem.com/products/ti17.html
This study aims to predict poor glycemic control during Ramadan among non-fasting patients with diabetes using machine learning models. First, we conducted three consultations, before, during, and after Ramadan to assess demographics, diabetes history, caloric intake, anthropometric and metabolic parameters. Second, machine learning techniques (Logistic Regression, Support Vector Machine, Naive Bayes, K-nearest neighbor, Decision Tree, Random Forest, Extra Trees Classifier and Catboost) were trained using the data to predict poor glycemic
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