Abstract: Diabetes is a chronic condition that affects how the body processes blood sugar, leading to consistently high blood glucose levels. Predicting diabetes early can go a long way in reducing ...

Passive sensing via wearable devices and smartphones, combined with machine learning (ML), enables objective, continuous, and noninvasive mental health monitoring. Objective: This study aimed to ...

Our findings suggest an increasing gap in diabetes prevalence between the most and least genetically susceptible. This suggests that people with a high genetic susceptibility to type 2 diabetes could ...

Can deep learning catch chronic illness before symptoms show? This article explores how time-aware neural networks are reshaping early detection and care planning for conditions like diabetes and COPD ...

Researchers develop an AI tool to predict cardiometabolic multimorbidity risk in type 2 diabetes, aiding early intervention and personalised care. Find out more.

Nuclear fuel performance is critically dependent on understanding the evolution of fuel properties under operational conditions, a complex challenge driven by chemical changes and substantial ...

Abstract: This study evaluated three machine learning algorithms in predicting a diabetes mellitus diagnosis using a publicly available health data set. The models developed and analyzed in this study ...

Objective: Analyze the psychological and clinical factors of clinically significant tinnitus (THI score ≥38) in patients with hearing loss, construct predictive models based on four machine learning ...

Background: Coronary artery disease (CAD) demonstrates a strong bidirectional association with diabetes mellitus, which not only elevates cardiovascular disease risk but also correlates with poorer ...

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 ...