The American Journal of Managed Care

Machine Learning May Enable Earlier Detection of CKD Risk Factors

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...
The Punch

Every Bet You Place Has an Algorithm Behind It

Discover the AI in sports betting that powers real-time odds, personalizes your experience, and enhances security. Learn how algorithms run the show.
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 ...

Fast AI for satellite learns to quantify uncertainty

Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of ...
IEEE

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation

Abstract: Inner speech recognition has gained enormous interest in recent years due to its applications in rehabilitation, developing assistive technology, and cognitive assessment. However, since ...
Scientific Research Publishing

Amma N.G., B. (2024) En-RfRsK: An Ensemble Machine Learning Technique for Prognostication of Diabetes Mellitus. Egyptian Informatics Journal, 25, Article 100441.

ABSTRACT: The early detection of type 2 diabetes is a major challenge for healthcare professionals, as a late diagnosis can lead to severe and difficult-to-manage complications. In this context, this ...
labs.sogeti

MACHINE LEARNING ALGORITHMS FOR TEST AUTOMATION

The emergence of using Machine Learning Techniques in software testing started in the 2000s with the rise of Model-Based Testing and early bug prediction models trained on historical defect data. It ...