Back to search

Article

Glucobuddy: Detecting Diabetes Risk Using Machine Learning

2025-05-28

Abstract excerpt

Diabetes mellitus is a chronic disease affecting over 420 million people globally, contributing significantly to mortality, disability, and healthcare costs. Early detection and risk assessment are critical in preventing severe complications such as cardiovascular disease, kidney failure, and neuropathy. Traditional diagnostic approaches, including fasting glucose and HbA1c testing, require medical infrastructure...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
d1d0077e-6614-55ab-8f40-24bb417cb97a
DOI
10.20944/preprints202505.2269.v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Glucobuddy: Detecting Diabetes Risk Using Machine LearningDOI 10.20944/preprints202505.2269.v1
Diabetes Prediction Using Machine LearningSemantic proximity
Hybrid Artificial Intelligence and Large Language Model-Based Digital Health Platform for Personalized Diabetes Self-Management and Patient EngagementSemantic proximity
WITHDRAWN: A Machine Learning Model for Real-Time Hypoglycemia Risk Prediction in Hospitalized Diabetic Patients: Development and ValidationSemantic proximity
Development and validation of a type 2 diabetes machine learning classification model for EHR-based diagnostics and clinical decision supportSemantic proximity
Can Artificial Intelligence Counsel Expectant Mothers? Expert Evaluation of ChatGPT’s Educational Performance in Gestational Diabetes MellitusSemantic proximity
Predicting Unplanned Medical Visits Among Patients with Diabetes: Translation From Machine Learning to Clinical ImplementationSemantic proximity
Towards Reliable Diabetes Prediction: Innovations in Data Engineering and Machine Learning ApplicationsSemantic proximity
A Machine Learning–Driven Health Risk Index for Predicting Chronic Disease BurdenSemantic proximity
Trust in large language model-based solutions in healthcare among people with and without diabetes: a cross-sectional survey from the Health in Central Denmark cohortSemantic proximity
A Systematic Review of Hybrid Intelligent Models for Early Detection of Diabetes Using Electronic Health RecordsSemantic proximity
Artificial Intelligence in Dietary Recommendations for Type 1 Diabetes: Evaluating the Performance of ChatGPT-4o, Bing AI, and Bard AISemantic proximity
Predicting the risks for stroke, cardiovascular disease, and peripheral vascular disease among people with type 2 diabetes with artificial intelligence models: a systematic review and meta-analysisSemantic proximity
Select a neighboring publication to make it the new centre.