This project presents a machine learning-based predictive model developed in MATLAB to forecast hospital resource requirements using historical healthcare data. The system helps in predicting future ...

While machine learning (ML) has garnered increasing attention in health care applications, effective early prediction tools remain limited in current clinical practice. Recent investigations have ...

1 Department of Mathematics & Statistical Sciences, Jackson State University, Jackson, MS, USA. 2 Department of Public Health, California State University, Fullerton ...

1. An integrative machine learning predictive model based on individual base models can accurately predict the incidence of anastomotic leakage (AL) after colon resection. AL after colorectal surgery ...

Background: Machine learning (ML) has been investigated for its predictive value in knee osteoarthritis (KOA) progression. However, systematic evidence on the effectiveness of ML is still lacking, ...

A total of 8,598 children were enrolled and classified into three groups: ADHD (n=3,678), subthreshold ADHD (s-ADHD) (n=1,495), and healthy controls (HC) (n=3,425). Data collection covered 40 ...

Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field ...

Abstract: This work investigates a hybrid quantum-classical machine learning methodology that combines deep learning with quantum computing to improve predictive analytics. The method starts by ...