Open Heart

Artificial intelligence-based clustering to identify functional risk phenotypes in heart failure

Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...
The Next Web

AI training efficiency: From Throughput to Goodput

Pretraining a modern large language model (LLM), often with ~100B parameters or more, typically involves thousands of ...
Scientific Research Publishing

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ()

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Open Heart

Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...
Devdiscourse

AI systems can create competing ‘algorithmic worlds’ from same data

AI & Society, states that algorithmic systems often construct competing but equally valid “model-worlds,” offering empirical support for a philosophical claim that evidence alone cannot uniquely ...
TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...
The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
Visual Studio Magazine

Linear Regression with Pseudo-Inverse Training Using JavaScript

Linear regression is the most fundamental machine learning technique to create a model that predicts a single numeric value. One of the three most common techniques to train a linear regression model ...
Scientific Research Publishing

Heart Disease Prediction: A Logistic Regression Approach ()

1 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA. 2 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA. 3 ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...
Frontiers

Postpartum depression risk prediction using explainable machine learning algorithms

Postpartum depression (PPD) is a common and serious mental health complication after childbirth, with potential negative consequences for both the mother and her infant. This study aimed to develop an ...
NKyTribune

CPE, DCBS launch new Community Learning Network to train health services professionals

The Kentucky Council on Postsecondary Education (CPE), in partnership with the Department for Community-Based Services (DCBS) and a consortium of postsecondary institutions, kicked off the Community ...