Master of Science (MS)
First Committee Member
Second Committee Member
Third Committee Member
Fourth Committee Member
Fifth Committee Member
Number of Pages
Osteoporosis becomes very common problem for people after a certain age, which results in fragility fractures without any previous symptoms. One of the primary predictors of osteoporosis is bone mineral density (BMD). BMD is the mineral content of bone, at the optimal levels, that makes the bone strong enough to bear the regular load and elastic enough to handle the irregular twisting load. Two of the major parts of the bone that help to acquire such property are trabecular and cortical bone. This thesis focuses on predicting the BMDs of trabecular and cortical bone for men. For this purpose we performed Genome Wide Association Study (GWAS) for quality control and obtained new subsets of 537 and 536 Single Nucleotide Polymorphisms (SNPs) associated with trabecular and cortical BMDs. Various machine learning algorithms were used for the predictive analysis, among which linear regression (LR), support vector machine (SVM) and multi-layer perceptron (MLP) gave much better results with the newly obtained subset of SNPs, compared to the results using the 1103 and 307 SNPs associated with BMD found in the existing literature. LR gave mean squared error (MSE) of 0.000658 and coefficient of determination (r2) of 0.643479, SVM gave MSE of 0.000628 and r2 of 0.65971, and MLP gave MSE 0.000683 and r2 0.62989 for trabecular BMD with 537 SNPs. Similarly, LR, SVM, and MLP gave MSEs of 0.001109, 0.001103, and 0.00112, and r2 of 0.707548, 0.709079 and 0.703947, respectively, for cortical BMD with 536 SNPs. In both cases, SVM gave better results.
Osteoporosis; Bone mineral density (BMD); Genome Wide Association Study (GWAS); Single Nucleotide Polymorphisms (SNPs)
University of Nevada, Las Vegas
Chudal, Partha, "Machine Learning for Prediction of Trabecular and Cortical Bone Mineral Density" (2020). UNLV Theses, Dissertations, Professional Papers, and Capstones. 3878.
IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/