In A Nutshell Researchers used a machine learning model to rank all 50 U.S. states and Washington, D.C. by socioeconomic vulnerability to flu-like illness, finding wide regional variation in risk.

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...

This paper proposes a hybrid machine learning framework for early diabetes prediction tailored to Sierra Leone, where locally representative datasets are scarce. The framework integrates Random Forest ...

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 ...

This project aims to build a machine learning model that accurately predicts whether a patient has diabetes based on medical measurements. The dataset is highly imbalanced (65% non-diabetic vs 35% ...

A machine learning algorithm used gene expression profiles of patients with gout to predict flares. The PyTorch neural network performed best, with an area under the curve of 65%. The PyTorch model ...

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, ...

Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as the Framingham ...

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 ...

The final, formatted version of the article will be published soon. Background): Diabetes Mellitus (DM) is a chronic metabolic disorder that poses a significant global health challenge, affecting ...