Ensemble-learning approach improves fracture prediction using genomic and phenotypic data.

ABSTRACT: This study presents an innovative ensemble machine learning model integrating genomic and clinical data to enhance the prediction of major osteoporotic fractures in older men. The Super Learner (SL) model achieved superior performance (AUC = 0.76, accuracy = 95.6%, sensitivity = 94.5%, specificity = 96.1%) compared to individual models. Ensemble machine learning improves fracture prediction accuracy, demonstrating the potential for personalized osteoporosis management.

Purpose

Existing fracture risk models have limitations in their accuracy and in integrating genomic data. This study developed and validated an innovative ensemble machine learning (ML) model that combines multiple algorithms and integrates clinical, lifestyle, skeletal, and genomic data to enhance prediction for majo

SUBMITTER: Wu Q 

PROVIDER: S-EPMC12089207 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature