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Improving Type 2 Diabetes Phenotypic Classification by Combining Genetics and Conventional Risk Factors

Abdulaimma, B, Hussain, A, Fergus, P, Al-Jumeily, D, Lisboa, P, Huang, D-S and Radi, N Improving Type 2 Diabetes Phenotypic Classification by Combining Genetics and Conventional Risk Factors. In: 2018 IEEE Congress on Evolutionary Computation (IEEE CEC 2018), 08 July 2018 - 13 April 2018, Brazil. (Accepted)

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Abstract

Type 2 Diabetes condition is a multifactorial disorder involves the convergence of genetics, environment, diet and lifestyle risk factors. This paper investigates genetic and conventional (clinical, sociodemographic) risk factors and their predictive power in classifying Type 2 Diabetes. Six statistically significant Single Nucleotide Polymorphisms (SNPs) associated with Type 2 Diabetes are derived by conducting logistic association analysis. The derived SNPs in addition to conventional risk factors are used to model supervised machine learning algorithms to classify cases and controls in genome wide association studies (GWAS). Models are trained using genetic variable analysis, genetic and conventional variable analysis, and conventional variable analysis. The results demonstrate of the three models, higher predictive capacity is evident when genetic and conventional predictors are combined. Using a Random Forest classifier, the Area Under the Curve=73.96%, Sensitivity=68.42%, and Specificity=78.67%.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > R Medicine (General)
Divisions: Applied Mathematics
Computer Science
Publisher: IEEE
Date Deposited: 20 Apr 2018 09:16
Last Modified: 20 Apr 2018 09:16
URI: http://researchonline.ljmu.ac.uk/id/eprint/8551

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