An artificial intelligence (AI)-powered lifestyle intervention app for prediabetes reduces the risk for diabetes similarly to ...

Wearables, Mobile Health (m-Health), Real-Time Monitoring Share and Cite: Alqarni, A. (2025) Analysis of Decision Support ...

Researchers successfully developed a machine learning-based method for predicting symptom deterioration in patients with cancer.

IOP Publishing’s Machine Learning series is the world’s first open-access journal series dedicated to the application and ...

Risk stratification of patients with chest pain has traditionally focused on identifying obstructive coronary artery disease (CAD). Using this traditional approach, many symptomatic individuals are ...

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, ...

Prostate cancer (PCa) is one of the most common malignancies in men, and accurate assessment of tumor aggressiveness is crucial for treatment planning. The Gleason score (GS) remains the gold standard ...

Though they’re ultimately two different lenses, sports betting can provide some smart intel for fantasy football and how players are expected to perform — after all, sportsbooks do an incredible ...

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 ...

It is unclear who benefits the most from atherosclerotic cardiovascular disease (ASCVD) screening imaging. This study aimed to identify features associated with positive coronary artery calcium scores ...

ABSTRACT: This paper aims to investigate the effectiveness of logistic regression and discriminant analysis in predicting diabetes in patients using a diabetes dataset. Additionally, the paper ...