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 ...
IEEE

Predicting Mortality Risk in Heart Failure Patients: Insights from COX Regression and Machine Learning

Abstract: This study is based on baseline data and 2-year follow-up information from 145 heart failure patients in Guangxi, China, combined with a publicly available scientific dataset on Chinese ...
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 ...
Psychology Today

Diagnosing Schizophrenia With Machine Learning

Machine learning can predict many things, but can it predict who will develop schizophrenia years before the average ...
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 ...
IEEE

Mitigating Unfair Regression in Machine Learning Model Updates

Abstract: Machine learning systems often require updates for various reasons, such as the availability of new data or models and the need to optimize different technical or ethical metrics. Typically, ...