Analyzing the Mutation Frequencies and Correlation of Genetic Diseases in Worldwide Populations Using Big Data Processing, Clustering, and Predictive Analytics

In this paper, we utilize Big Data Processing and develop Predictive Analytics Models to examine and analyze mutations associated with osteoporosis and cardiovascular disease. The dataset consists of the genomic information of over 2,500 individuals. The genomic data was collected from all around the world. The data visualization allowed us to see geographical/regional clustering patterns in the above mentioned specific mutations. The visualized data clearly shows a high correlation between a person’s regional background and the occurrence of the 35 single nucleotide polymorphisms (SNPs). The 35 SNPs are specifically associated with osteoporosis and/or cardiovascular disease (CVD). A predictive analytics model was developed based on machine learning algorithms to predict the risk of an individual manifesting osteoporosis in later life. The results of this predictive model confirmed the links between osteoporosis and Cardiovascular related parameters such as High Density Lipoprotein (HDL) and Systolic Blood Pressure (SBP), as determined by the preceding studies.