SNPs-based hypertension disease detection via machine learning techniques

Raid Alzubi, Naeem Ramzan, Hadeel Alzoubi, Stamos Katsigiannis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine learning and data mining techniques have recently gained more popularity in the field of Medical diagnosis, especially for the analysis of the human genome. One of the most significant sources of human genome variation is Single Nucleotide Polymorphisms (SNPs), which have been associated with multiple human diseases. Several techniques have been developed for distinguishing between affected and healthy samples of SNP data. In this study, conditional mutual information maximisation (CMIM) has been employed in order to identify a subset of the most informative SNPs to be used in with various classifications algorithms for the detection of hypertension disease. Five classification algorithms have been evaluated, namely k-Nearest Neighbours (KNN), Artificial Neural Networks (ANN), Naive Bayes (NB), Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM), along with their combination into an unweighted majority voting ensemble classification scheme. The experimental evaluation of the proposed approach via supervised classification experiments showed that the ensemble approach using the SVM, 5-NN, and NB classifiers achieves the highest classification accuracy (93.21%) and F1 score (91.72%), demonstrating the suitability of the proposed approach for the detection of hypertension disease from SNPs data.
Original languageEnglish
Title of host publication2018 24th IEEE International Conference on Automation and Computing
EditorsXiandong Ma
PublisherIEEE
Number of pages6
ISBN (Electronic)9781862203426
DOIs
Publication statusPublished - 6 Sep 2018
Event24th International Conference on Automation and Computing - Newcastle upon Tyne, United Kingdom
Duration: 6 Sep 20187 Sep 2018
http://www.cacsuk.co.uk/index.php/conferences/icac/program

Conference

Conference24th International Conference on Automation and Computing
Abbreviated titleICAC2018
CountryUnited Kingdom
CityNewcastle upon Tyne
Period6/09/187/09/18
Internet address

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  • Cite this

    Alzubi, R., Ramzan, N., Alzoubi, H., & Katsigiannis, S. (2018). SNPs-based hypertension disease detection via machine learning techniques. In X. Ma (Ed.), 2018 24th IEEE International Conference on Automation and Computing IEEE. https://doi.org/10.23919/IConAC.2018.8748972