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Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men

Calcified Tissue International volume 107, pages 353–361 (2020)Cite this article

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.

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Abbreviations

MrOS:

Osteoporotic fractures in men study

ML:

Machine learning

BMD:

Bone mineral density

FRAX:

The fracture risk assessment tool

GRS:

Genetic risk score

QUS:

Quantitative ultrasound

ROC:

Receiver-operating curve

AUC:

Area under curve

LR:

Logistic regression

RF:

Random forest

GB:

Gradient boosting

NN:

Neural network

MOF:

Major osteoporotic fracture

SNPs:

Single nucleotide polymorphisms

FNBMD:

Femoral neck BMD

TSBMD:

Total spine BMD

THBMD:

Total hip BMD

SOS:

Speed of sound

BUA:

Broadband ultrasonic attenuation

QUI:

Quantitative ultrasonic index

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Acknowledgements

The data/analyses presented in the current publication are based on the use of study data downloaded from the dbGaP web site, under phs000373.v1.p1 (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000373.v1.p1). The research and analysis described in the present study were supported by a COBRE grant from the National Institute of General Medical Sciences (GR08954), the Genome Acquisition to Analytics (GAA) Research Core of the Personalized Medicine Center of Biomedical Research Excellence at the Nevada Institute of Personalized Medicine, and the National Supercomputing Institute at the University of Nevada Las Vegas. The funding sponsors were not involved in the analysis design, genotype imputation, data analysis, and interpretation of the analysis results or the preparation, review, or approval of this manuscript.

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Affiliations

  1. Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, NV, 89154-4009, USA

    Qing Wu, Jongyun Jung & Mira V. Han

  2. Department of Epidemiology and Biostatistics, School of Public Health, University of Nevada, Las Vegas, NV, USA

    Qing Wu & Jongyun Jung

  3. Department of Computer Science, University of Nevada, Las Vegas, NV, USA

    Fatma Nasoz & Bibek Bhattarai

  4. The Lincy Institute, University of Nevada, Las Vegas, NV, USA

    Fatma Nasoz

  5. School of Life Sciences, University of Nevada, Las Vegas, NV, USA

    Mira V. Han

Authors
  1. Qing Wu
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  2. Fatma Nasoz
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  3. Jongyun Jung
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  4. Bibek Bhattarai
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Corresponding author

Correspondence to Qing Wu.

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Conflict of interest

Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai and Mira V Han declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This study analyzed de-identified, secondary data only, and was exempted by the Institutional Review Board at the University of Nevada, Las Vegas.

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Wu, Q., Nasoz, F., Jung, J. et al. Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men. Calcif Tissue Int 107, 353–361 (2020). https://doi.org/10.1007/s00223-020-00734-y

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  • DOI: https://doi.org/10.1007/s00223-020-00734-y

Keywords

  • Machine learning
  • Fracture
  • Osteoporosis
  • Genomics
  • Comparison