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Predictive Analytics to Determine the Potential Occurrence of Genetic Disease and their Correlation: Osteoporosis and Cardiovascular Disease

Authors:
Kae Sawada
Michael W. Clark
Zilong Ye
Nabil Alshurafa
Mohammad Pourhomayoun

Keywords: osteoporosis; Predictive Model; Genome Wide Association Study (GWAS); Clustering

Abstract:
In this paper, a Predictive Analytics Model is designed, developed, and validated to determine the risk of manifesting osteoporosis in later life using big data processing. The proposed model leverages the novel genetic pleiotropic information in the 1,000 Genome Project of over 2,500 individuals world-wide. Also, the mutations associated with osteoporosis and cardiovascular disease are specifically analyzed. The study proposes the automatic histogram clustering as an effective and intuitive visualization method for high dimensional dataset. The results demonstrate a significant correlation between a person’s regional background and the frequency of occurrence of the 35 Single Nucleotide Polymorphisms (SNPs) associated with osteoporosis and/or cardiovascular disease (CVD). Machine learning algorithms, such as Logistic Regression, Adaboost, and KNN are then applied to predict the occurrence of 7 osteoporosis-related-SNPs based on the existing CVD-related-SNPs input. Finally, the developed model is evaluated using a separate dataset obtained through Affymetrix microarray mRNA expression signal values for the specific SNP(s) in individuals with and without osteoporosis.

Pages: 1 to 7

Copyright: Copyright (c) IARIA, 2018

Publication date: May 20, 2018

Published in: conference

ISSN: 2308-4383

ISBN: 978-1-61208-639-2

Location: Nice, France

Dates: from May 20, 2018 to May 24, 2018