Advances in artificial intelligence (AI) are now opening new possibilities for faster and more accurate flood mapping, enabling researchers to process large volumes of environmental data and satellite ...

A new study suggests that lenders may get their strongest overall read on credit default risk by combining several machine learning models rather than relying on a single algorithm. The researchers ...

The promise of artificial intelligence in credit scoring is undeniable. By analyzing vast, non-traditional datasets from ...

Medicaid managed care organizations should prioritize children in low-opportunity neighborhoods to optimize health care utilization, improve minority health, and address health-related social needs.

Objectives To evaluate whether type 2 diabetes mellitus (T2DM) presence and severity are associated with differences in global and domain-specific cognitive function among US adults, using ...

A new machine learning model built using a simple and interpretable approach predicts in-hospital death in patients with acute liver failure and reveals top risk drivers.

Background Remission and low-disease activity are recommended targets in systemic lupus erythematosus (SLE), yet many ...

Background Cardiovascular disease (CVD) is the leading cause of mortality worldwide, while depression is highly prevalent in this patient population and has long been regarded as an independent 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 project aims to detect and classify 16x16 pixel drawings into 10 categories (Sun, Moon, Tree, etc.) using linear and probabilistic models. The main focus was not just to use high-level libraries, ...

Abstract: In this project, we aimed to assess mushroom contamination by analyzing images using two different algorithms: a novel K-Nearest Neighbour algorithm and a traditional Logistic Regression ...