Machine learning approaches for the prediction of bone mineral density by using genomic and phenotypic data of 5130 older men.

ABSTRACT: The study aimed to utilize machine learning (ML) approaches and genomic data to develop a prediction model for bone mineral density (BMD) and identify the best modeling approach for BMD prediction. The genomic and phenotypic data of Osteoporotic Fractures in Men Study (n = 5130) was analyzed. Genetic risk score (GRS) was calculated from 1103 associated SNPs for each participant after a comprehensive genotype imputation. Data were normalized and divided into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and linear regression were used to develop BMD prediction models separately. Ten-fold cross-validation was used for hyper-parameters optimization. Mean square error and mean absolute error were used to assess model performance

SUBMITTER: Wu Q 

PROVIDER: S-EPMC7904941 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature