Supervised learning is the most common form of machine learning utilized in medical
research. It is used to predict outcomes of interest or classify positive and/or negative cases …

Background Machine learning offers new solutions for predicting life-threatening,
unpredictable amiodarone-induced thyroid dysfunction. Traditional regression approaches …

The uncertainty of the environment, the complexity of economic systems, both at the national
and global economy levels, and the digital age and artificial intelligence draw attention to …

STUDY QUESTION Can two prediction models developed using data from 1999 to 2009
accurately predict the cumulative probability of live birth per woman over multiple complete …

Whether a new technology can spread smoothly in the market heavily depends on the user's
acceptance of the technology. A considerable number of studies have sought to predict user …

The etiology of chronic bowel disorders is multifaceted, with environmental exposure to
harmful substances potentially playing a significant role in their pathogenesis. However …

Metabolic syndrome (MetS) is a cluster of risk factors including hypertension, hyperglycemia,
dyslipidemia, and abdominal obesity. Metabolism-related risk factors include diabetes and …

Purpose Risk prediction models are frequently used to support decision-making in colorectal
surgery but can be inaccurate. Machine learning (ML) is becoming increasingly popular, and …

BACKGROUND: The American College of Surgeons NSQIP risk calculator (RC) uses
regression to make predictions for fourteen 30-day surgical outcomes. While this approach …

Peripheral artery disease (PAD) biomarkers have been studied in isolation; however, an
algorithm that considers a protein panel to inform PAD prognosis may improve predictive …