Ensemble-learning approach improves fracture prediction using genomic and phenotypic data
1Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, USA. Qing.Wu@osumc.edu.
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 major osteoporotic fractures (MOF) in older men.
Methods:
This study analyzed data from 5130 participants in the Osteoporotic Fractures in Men cohort Study. The model incorporated 1103 individual genome-wide significant variants and conventional risk factors of MOF. The participants were randomly divided into training (80%) and testing (20%) sets. Seven ML algorithms were combined using the SL ensemble method with tenfold cross-validation MOF prediction. Model performance was evaluated on the testing set using the area under the curve (AUC), the area under the precision-recall curve, calibration, accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and reclassification metrics. SL model performances were evaluated by comparison with baseline models and subgroup analyses by race.
Results:
The SL model demonstrated the best performance with an AUC of 0.76, accuracy of 95.6%, sensitivity of 94.5%, specificity of 96.1%, NPV of 95.1%, and PPV of 94.7%. Among the individual ML, gradient boosting performed optimally. The SL model outperformed baseline models, and it also achieved accuracies of 93.1% for Whites and 91.6% for Minorities, outperforming single ML in subgroup analysis.
Conclusion:
The ensemble learning approach significantly improved fracture prediction accuracy and model performance compared to individual ML. Integrating genomic and phenotypic data via the SL approach represents a promising advancement for personalized osteoporosis management.
