IEEE

Evaluation of A Hybrid Approach Combining Random Forest and XGBoost for Type 2 Diabetes Prediction Using the PIMA Dataset

Abstract: This study evaluates the performance of three machine learning models in predicting type 2 diabetes, focusing on their accuracy, sensitivity, and generalization capacity. The methodological ...

AI-powered electrocardiogram detects early signs of heart failure

Interpreting relatively inexpensive electrocardiograms (ECGs) with an artificial intelligence (AI) algorithm accurately ...
IEEE

DGHME:A Hierarchical Hybrid Expert Multi-task Learning Model for Disease Grouping in Diabetes Complication Prediction

Abstract: Accurate identification and prediction of diabetes complications contribute to improved patient health. However, existing prediction models predominantly employ single-task learning (STL) ...

UC San Diego study uses machine learning to dive deeper into diabetes risk

A new tool named T1GRS enables researchers to get more accurate, further-reaching risk scores for the greater population ...
Medical Device and Diagnostic Industry (MD+DI)

How Large Language Models Are Reshaping Health Prediction & Clinical Decision Making

Large Language Models (LLMs) such as GPT-4, Gemini-Pro, Llama 2, and medical-domain-tuned variants like Med-PaLM 2 have ...