Machine learning approaches for fracture risk assessment: a comparative analysis of genomic and phenotypic data in 5,130 older men

MedRxiv : the Preprint Server for Health Sciences
Qing WuM. V. Han

Abstract

The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n=5,130), was analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1,103 associated SNPs for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures separately, with GRS, bone density and other risk factors as predictors. For model training, the synthetic minority over-sampling technique was used to account for low fracture rate, and 10-fold cross-validation was employed for hyperparameters optimization. In the testing set, the area under the ROC curve (AUC) and accuracy were used to assess the model performance. The McNemar test was employed for pairwise comparisons to examine the accuracy difference between models. The results showed that the prediction performance of gradient boosting was the best, with AUC of 0.71 and an accuracy...Continue Reading

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