The Lancet

Machine learning prediction of 1-year mortality in older patients with heart failure: a nationwide, multicenter, prospective cohort study

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...
Open Heart

Bridging machine learning and clinical practice: a multicentre nomogram for 90-day graft failure risk stratification in heart transplantation

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...
Visual Studio Magazine

Linear Regression with Pseudo-Inverse Training Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...
IEEE

Reducing Unnecessary Handovers Using Logistic Regression in 5G Networks

Abstract: The evolution of wireless communication has brought great benefits to society, such as multi-connectivity, increased connection speed, low latency, and elevated throughput. However, it has ...
United States Army

Sustainment Proactivity: Using Data-Centric C2 to Predict Convoy Failures

Introduction You are tasked to lead a 40-truck convoy resupplying combat troops at the front during large-scale combat ...
IEEE

A Comprehensive Machine Learning Approach for Wild Fire Detection and Classification Using Squeezenet and Ridge Regularized Logistic Regression

Abstract: Wild fire is a serious environmental and socioeconomic menace and therefore any effort aimed at the early and accurate detection of wild fire has to be very useful. This paper proposes a ...