Understanding Homoskedasticity in Regression Modeling With Examples

Discover the importance of homoskedasticity in regression models, where error variance is constant, and explore examples that illustrate this key concept.
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 identification to improve prevention and management strategies. Traditional risk ...
IEEE

Regression and Machine Learning Methods for Predicting Human Movements Based on Skeletal Data

Abstract: In recent years, the advancement of computer vision technologies has facilitated the extraction of skeletal data from human movements captured in video sequences. Leveraging this wealth of ...
AHA/ASA Journals

Multistage Electrocardiographic Profiling for Mental Stress-Induced Myocardial Ischemia in Women: A Prospective, Age-Matched Cohort Study in China

BACKGROUND: Mental stress-induced myocardial ischemia is often clinically silent and associated with increased cardiovascular risk, particularly in women. Conventional ECG-based detection is limited, ...
IEEE

Systematic Bias of Machine Learning Regression Models and Correction

Machine learning models for continuous outcomes often yield systematically biased predictions, particularly for values that largely deviate from the mean. Specifically, predictions for large-valued ...