A Classification Approach Based on Genetic-Data-Structuring for the Prediction of Hypertension?


  • A. Anis Lahore College for Women University, Lahore
  • M. A. Fahiem Lahore College for Women University, Lahore,
  • H. Tauseef Lahore College for Women University, Lahore


Hypertension is known to be a major cause of death around the world. The death rate for this disease has increased up to 94% since last decade. Due to this disease, dangerous health conditions arise like heart failure, kidney failure and stroke. To prevent permanent loss, there is an imperious need for automated techniques to be developed for the detection of this disease. Recently, genetic information has been combined with machine learning techniques for the detection of hypertension disease. The genetics based diagnosis also plays a key role in evolution of this disease. In this paper both the genetic and demographic data is used forth detection of this disease. For detection purpose we have gathered genetic datasets of 250 patients from different databases (online repositories). Our research makes use of a feature set comprising genes, SNP alleles, risk alleles, chromosomes, region and nationality of patients. Here, an algorithm is proposed for data structuring. For performance evaluation of our proposed approach we have applied different types of classification models; naive bayes, filtered classifier, LWL, K* and NBF network. The results obtained show that best accuracy is achieved using naive bayes i.e. 98%.

Author Biographies

A. Anis, Lahore College for Women University, Lahore

Department of Computer Science

M. A. Fahiem, Lahore College for Women University, Lahore,

Department of Computer Science

H. Tauseef, Lahore College for Women University, Lahore

Department of Computer Science


D. Gasecki, M. Kwarciany, K. Kowalczyk, A. Rojek , T. Nowicki, M. Skrzypek-Czerko, E. Szurowska, P. Boutouyrie, S. Laurent and K. Narkiewicz, “Aortic stiffness is an independent biomarker of subclinical brain damage in acute ischemic stroke”, J. Hypertension, 33(Supp 1): pp. e1-e524, June 2015.

N. Hussain, D. Capener, C. Elliot, R. Condliffe, J. M. Wild, D. G. Kiely and A. Swift. “Interventricular septal angle can be used to predict which patients have combined post capillary or pre capillary pulmonary hypertension in left heart disease”, J. Cardiovascular Magnetic Resonance, 17, Supp. 1, p. 338,Feb 2015. [3] Y. Yano, J. Stamler, D.B. Garside, M.L. Daviglus, S.S. Franklin, M.R. Carnethon, K. Liu, P. Greenland and D. M. Lloyd-Jones, “Isolated systolic hypertension in young and middle-aged adults and 31-year risk for cardiovascular mortality”, J. American College of the Cardiology, vol. 65, no. 4, pp. 327-335, Feb 2015.

K. Chau, D. Holmes, A. Melck and C. Chan- Yan, “Secondary hypertension due to mutant aldosterone-producing adenoma and parathyroid adenoma”, American Journal of Hypertension, vol. 28, no. 2, pp. 280-282, Feb 2015.

A.P. Steinhorst, S.C. Gonçalves, A.T. Oliveira, D. Massierer, M. Gus, S.C. Fuchs, L.B. Moreira, D. Martinez and F.D. Fuchs, “Influence of sleep apnea severity on blood pressure variability of patients with hypertension, ”Sleep and Breating, vol. 18, no. 2, pp. 397-401, May 2014.

A. Harvey, A. C. Montezano and R.M. Touyz, “Vascular biology of ageing- Implications in hypertension, “Journal of Molecular and Cellular Cardiology, vol. 83, pp. 112–121, June 2015.

X. Xu, S. Su, F. A. Treiber, R. Vlietinck, R. Fagard, C. Derom, M. Gielen, J.F. Ruth. Loos, H. Snieder and X. Wang, “Specific genetic influences on night time blood pressure”, American Journal of Hypertension, vol. 28, no.4, pp. 440-443, April 2015.

J.L. Greaney, E.L. Matthews and M.M. Wenner, “Sympathetic reactivity in young women with a family history of hypertension”, American Journal of Physiology-Heart and Circulatory Physiology, vol. 308, no. 8, pp. H816-H22, April 2015.

A. Kitts, L. Phan, M. Ward and J. B. Holmes, “The database of short genetic variation (dbSNP)”, [ONLINE], Available: http://www.ncbi.nlm.nih.gov/. [10] P.D. Stenson, M. Mort, E. V. Ball, K. Howells, A. D. Phillips, N.S. Thomas and D.N. Cooper, “The human genome mutation database: 2008 update”, Genome Medicine, vol. 1, no. 1, pp. 1, Jan. 2009.

EMBL-EBI, “1000 genome”, The International genome sample resource”, European 2016, [ONLINE] http://browser.1000 genomes.org/index.html [Accessed August 6, 2016].

“NIH Genetic Sequence Database”, Japan 2013, [ONLINE] https://www.ncbi.nlm.nih.gov/genbank/ [Accessed June 7, 2016].

“GenomeWide–Association Study”. [ONLINE] https://www.ebi. ac.uk/gwas/ [Accessed, March 4, 2016].

“Genome” [ONLINE], USA 2015, https://www.genome.gov/, [Accessed April 23, 2016].

A.A. Aljumah, M.G. Ahamad and M.K. Siddiqui, “Predictive analysis on hypertension treatment using data mining approach in Saudi Arabia”, Intelligent Information Management”, vol. 3, No. 6, pp. 252-261, Nov. 2011.

A. Kaur and A. Bhardwaj, “Artificial Intelligence in hypertension diagnosis: A review”, Int. J. Comp. Sci. Inf. Technol., vol. 5, no. 2, pp. 2633-2635, April 2014.

R. Samant and S. Rao, "Evaluation of artificial neural networks in prediction of essential hypertension", Int. J. Comp. Appl., vol. 81, no. 12, pp. 34-38, Nov. 2013.

X.Y. Djam and Y.H. Kimbi."Fuzzy expert system or the management of hypertension", The Pacific Journal of Science and Technology, vol. 12, no.1, pp. 390-402, May 2011.

N. Shehu, S.U. Gulumbe and H.M. Liman, "Comparative study between conventional statistical methods and neural networks in predicting hypertension status", Advances in Agriculture, Sciences and Engineering Research, vol. 3, no. 5, pp. 867-874, 2013.

Z. Abrishami and H. Tabatabaee, “Design a fuzzy expert system and a multi-layer neural network system for diagnosis for hypertension”, MAGNT Research Report, vol. 4, no.11, pp. 138-145, October 2015.

A.A. Abdullah, Z. Zakaria and N. F. Mohammad, "Design and development of fuzzy expert system for diagnosis of hypertension", Second International Conference on Intelligent Systems, Modelling and Simulation, Kuala Lumpur, Malaysia and Phnom Penh, Cambodia, pp. 113-117, January, 2011.

S. Das, P.K. Ghosh and S. Kar, "Hypertension diagnosis: A comparative study using fuzzy expert system and neuro fuzzy system", Fuzzy Systems, IEEE Int. Conf., Hyderabad, India, pp. 1-7, July,2013.

A. Blinowska, G. Chatellier, J. Bernier and M. Lavril, “Bayesian Statistics as applied to hypertension diagnosis”, IEEE Transactions on Biomedical Engineering, vol. 38, no. 7, pp. 699-706, July1991.

B. Krawczyk and M. Wozniak, “Hypertension diagnosis using compound pattern recognition method”, J. Med. Inf. Technol., vol.18, pp. 41-50, 2011.

M. Wozniak, “Two-Stage classifier for diagnosis of hypertension type”, Lecture Notes in Bioinformatics, Springer Berlin Heidelberg, vol. 4345, pp.433-440, 2006. [26] X. Yang, J. He, D. Gu, J.E. Hixson, J. Huang, D.C.Rao, L.C. Shimmin, J. Chen, T. K. Rice, J. Li and K.S. Kelly, “Associations of epithelial sodium channel genes with blood pressure changes and hypertension incidence: The Gen Salt Study”, American Journal of Hypertension, ISSN1941-7225, pp. hpu060, April 2014. [27] H. Izawa, Y. Yamada, T. Okada, M. Tanaka, H. Hirayama and M. Yokota, "Prediction of genetic risk for hypertension", Hypertension, vol.41, no.5, pp.1035-1040, May2003. [28] C. Chiu, K.H. Hsu, P.L. Hsu, C.I. Hsu, P.C. Lee, W.K. Chiou, T.H. Liu, Y.C. Chuang and C.J. Hwang, "Mining three-dimensional anthropometric body surface scanning data for hypertension detection", IEEE Transactions on Information Technology in Biomedicine, vol.11, no.3, pp.264-273, May2007. [29] Y.H. Choi, R. Chowdhury and B. Swaminathan, “Prediction of hypertension based on the genetic analysis of longitudinal phenotypes: A comparison of different modeling approaches for the binary trait of hypertension”, BMC Proc., vol. 8, no. 1, June 2014. [30] N. K. Lim, J. Y. Lee, J.Y. Lee, H.Y. Park and M. C. Cho, “The role of genetic risk score in predicting the risk of hypertension in the Korean population: Korean genome and epidemiology study”, PloSOne, vol. 10, no.6, June 2015. [31] N. Hemimi, A.A. Mansour and M.M. Abdelsalam, “Prediction of the risk for essential hypertension among carriers of c825t genetic polymorphism of g protein β3(GNB3) gene”, Biomarker Insights, vol. 11, pp.69–75, May 2016.




How to Cite

A. Anis, M. A. Fahiem, and H. Tauseef, “A Classification Approach Based on Genetic-Data-Structuring for the Prediction of Hypertension?”, The Nucleus, vol. 53, no. 4, pp. 236–242, Dec. 2016.