Improving T2D machine learning-based prediction accuracy with SNPs and younger age

Improving T2D machine learning-based prediction accuracy with SNPs and younger age

Background: This study aimed to evaluate whether integrating clinical and genomic data improves the performance of machine learning (ML) models for predicting Type 2 Diabetes (T2D) risk. Methods: Six models—Random Forest, Support Vector Machine, Linear Discriminant Analysis, Logistic Regression, Gra...

Full description

Saved in:
Bibliographic Details
Main Authors: Cynthia AL Hageh, Andreas Henschel, Hao Zhou, Jorge Zubelli, Moni Nader, Stephanie Chacar, Nantia Iakovidou, Haralampos Hatzikirou, Antoine Abchee, Siobhán O’Sullivan, Pierre A. Zalloua
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Computational and Structural Biotechnology Journal
Subjects:
T2D
Machine Learning
AI
Predictive models
Online Access:http://www.sciencedirect.com/science/article/pii/S2001037025002533
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S2001037025002533

Similar Items