biostudies-literatureWu QNational Institute of AgingNIA NIH HHSNIMHD NIH HHSNational Institute on Minority Health and Health DisparitiesNational Institute of General Medical SciencesNIGMS NIH HHS462-472https://www.ebi.ac.uk/biostudies/studies/S-EPMC11262147biostudies-literatureUnknown39(4)This study aimed to enhance the fracture risk prediction accuracy in major osteoporotic fractures (MOFs) and hip fractures (HFs) by integrating genetic profiles, machine learning (ML) techniques, and Bayesian optimization. The genetic risk score (GRS), derived from 1,103 risk single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS), was formulated for 25,772 postmenopausal women from the Women's Health Initiative dataset. We developed four ML models: Support Vector Machine (SVM), Random Forest, XGBoost, and Artificial Neural Network (ANN) for binary fracture outcome and 10-year fracture risk prediction. GRS and FRAX clinical risk factors (CRFs) were used as predictors. Death as a competing risk was accounted for in ML models for time-to-fracture data. ML models weJournal of bone and mineral research : the official journal of the American Society for Bone and Mineral ResearchEnhanced osteoporotic fracture prediction in postmenopausal women using Bayesian optimization of machine learning models with genetic risk score.PMC11262147R21 MD013681R01 AG080017R21MD013681P20 GM121325P20GM121325R01AG080017Wu QDai JfalseEnhanced osteoporotic fracture prediction in postmenopausal women using Bayesian optimization of machine learning models with genetic risk score.This study aimed to enhance the fracture risk prediction accuracy in major osteoporotic fractures (MOFs) and hip fractures (HFs) by integrating genetic profiles, machine learning (ML) techniques, and Bayesian optimization. The genetic risk score (GRS), derived from 1,103 risk single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS), was formulated for 25,772 postmenopausal women from the Women's Health Initiative dataset. We developed four ML models: Support Vector Machine (SVM), Random Forest, XGBoost, and Artificial Neural Network (ANN) for binary fracture outcome and 10-year fracture risk prediction. GRS and FRAX clinical risk factors (CRFs) were used as predictors. Death as a competing risk was accounted for in ML models for time-to-fracture data. ML models we2024-01-01T00:00:00Z2024 May2025-04-05T11:34:25.563Z2025-04-05T11:34:25.563ZS-EPMC112621473847774110.1093/jbmr/zjae025