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 ...

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 ...
IEEE

A Supervised Machine Learning-Based System for Diabetes Type Classification And Personalized Diet Recommendation

Abstract: In this paper, we describe a personalized diet recommendation system and an automatic diabetes classification system for the Pima Indians Diabetes Database with the help of eight important ...
Journal of Medical Internet Research

Beyond Comparing Machine Learning and Logistic Regression in Clinical Prediction Modelling: Shifting from Model Debate to Data Quality

The rapid uptake of supervised machine learning (ML) in clinical prediction modelling, particularly for binary outcomes based on tabular data, has sparked debate about its comparative advantage over ...
Frontiers

An explainable machine learning model predicts pediatric varicella encephalitis

Pediatric varicella encephalitis is a rare but serious complication of varicella, which has a significant impact on patient prognosis. Early clinical diagnosis is still challenging due to atypical ...