When natural disasters or extreme weather events hit, delivering aid quickly and efficiently to those affected is crucial.

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.

This is the largest real-world analysis of mycophenolic acid in pediatric lupus nephritis to date, providing a decision-support system to help balance efficacy and safety.

Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...

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 ...

ABSTRACT: The surge of digital data in tourism, finance and consumer markets demands predictive models capable of handling volatility, nonlinear dynamics, and long-term dependencies, where traditional ...

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, ...