The increasing prevalence of diabetes and its complications, such as cardiovascular and
kidney disease, remains a huge burden globally. Identification of biomarkers for the …

Objectives In this systematic review we aimed at assessing how artificial intelligence (AI),
including machine learning (ML) techniques have been deployed to predict, diagnose, and …

Objectives We aimed to review the literature regarding the use of machine learning to
predict chronic diseases. Study design This was a systematic review. Methods The searches …

Abstract Background Metabolic Syndrome (MetS) is characterized by risk factors such as
abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C) …

In numerous perilous cases, a quick medical decision is needed for the early detection of
chronic diseases to avoid austere consequences that may be fatal. Chronic kidney disease …

Large metabolomics datasets inevitably contain unwanted technical variations which can
obscure meaningful biological signals and affect how this information is applied to …

Diabetes type 2 remains a pressing worldwide health subject, highlighting the need for
advanced early detection methods. In this study, we performed a comprehensive analysis of …

Background Chronic kidney disease (CKD) is a global public health concern. Therefore, to
provide timely intervention for non-hospitalized high-risk patients and rationally allocate …

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to
diabetes and diabetic complications, and related substantial economic burden make …

Background Long-term exposure to air pollution has been associated with cardiopulmonary
diseases, while the underlying mechanisms remain unclear. Objectives To investigate …