Document Type
Article
Publication Date
7-29-2020
Publication Title
Calcified Tissue International
First page number:
1
Last page number:
9
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 = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms 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. In model training, the synthetic minority oversampling technique was used to account for low fracture rate, and tenfold cross-validation was employed for hyperparameters optimization. In the testing, the area under curve (AUC) and accuracy were used to assess the model performance. The McNemar test was employed 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 of 0.88, and the GRS ranked as the 7th most important variable in the model. The performance of random forest and neural network were also significantly better than that of logistic regression. This study suggested that improving fracture prediction in older men can be achieved by incorporating genetic profiling and by utilizing the gradient boosting approach. This result should not be extrapolated to women or young individuals.
Keywords
Machine learning; Fracture; Osteoporosis; Genomics; Comparison
Disciplines
Analytical, Diagnostic and Therapeutic Techniques and Equipment | Biomedical Devices and Instrumentation | Computer Engineering | Genomics | Geriatrics | Musculoskeletal System | Trauma
File Format
File Size
264 KB
Language
English
Repository Citation
Wu, Q.,
Nasoz, F.,
Jung, J.,
Bhattarai, B.,
Han, M. V.
(2020).
Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men.
Calcified Tissue International
1-9.
http://dx.doi.org/10.1007/s00223-020-00734-y
Included in
Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Biomedical Devices and Instrumentation Commons, Computer Engineering Commons, Genomics Commons, Geriatrics Commons, Musculoskeletal System Commons, Trauma Commons