Background Machine learning is increasingly used to predict healthcare outcomes,
including cost, utilization, and quality. Objective We provide a high-level overview of …

This paper develops and employs an ensemble machine learning (ML) model for prediction
of surface chloride concentration (C s) of concrete, which is an essential parameter for …

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue
characteristics and follow a well-understood path of technical, biological and clinical …

Mental disorders are among the leading causes of disability worldwide. The first step in
treating these conditions is to obtain an accurate diagnosis. Machine learning algorithms …

Decision trees and random forests are widely used classifiers in machine learning. Service
providers often host classification models in a cloud service and provide an interface for …

Context Automated classifiers, often based on machine learning (ML), are increasingly used
in software engineering (SE) for labelling previously unseen SE data. Researchers have …

Mastitis is the most common disease among dairy cows and is known to have negative
effects on both animal welfare and the profitability of dairy farms. Early detection of clinical …

Classification is a process that predicts class of objects whose class label is unknown.
According to No Free Lunch (NFL) theorem, there is no single classifier that performs better …

As witnessed by a vast corpus of literature, dimensionality reduction is a fundamental step
for biomedical data analysis. Indeed, in this domain, there is often the need for co** with a …

Computer aided diagnosis is a hot research field. Systems with the ability to provide a highly
accurate diagnosis using little resources are highly desirable. One type of such systems …