Introduction Maternal and child mortality has markedly decreased worldwide over the past few decades. Despite this success, the decline remains unequal across countries and is overall insufficient to ...
Hydrogels are among the most widely studied biomaterials in modern drug delivery research. Their high hydrophilicity, ...
Diabetes affects over 537 million adults globally, with early detection critical for effective treatment and management. This project develops a machine learning classification model to predict ...
Objective: To evaluate and to compare machine learning models for predicting hypertension in patients with diabetes using routine clinical variables. Methods: Using Behavioral Risk Factor Surveillance ...
AI is the broad goal of creating intelligent systems, no matter what technique is used. In comparison, Machine Learning is a specific technique to train intelligent systems by teaching models to learn ...
MASLD is prevalent in T2DM patients, with a 65% occurrence rate, and poses a higher risk for severe liver diseases. The study analyzed 3,836 T2DM patients, identifying key predictors like BMI, ...
Introduction Frailty is a common condition in older adults with diabetes, which significantly increases the risk of adverse health outcomes. Early identification of frailty in this population is ...
Abstract: Nutritional intervention can improve glycemic control for type 2 diabetes mellitus (T2DM), and thus accurately predicting post-prandial glycemic responses (PPGRs) to each meal is essential.
Background: Early identification of Type 1 Diabetes Mellitus (T1DM) in pediatric populations is crucial for implementing timely interventions and improving long-term outcomes. Peripheral blood ...
Objective: This study aims to develop and validate a machine learning model that integrates dietary antioxidants to predict cardiovascular disease (CVD) risk in diabetic patients. By analyzing the ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results