Classification of Primary Open Angle Glaucoma through Genetic and Demographic Data
Primary Open Angle Glaucoma (POAG) is considered to be one of the leading causes of irreversible blindness. More than 66 million people lie in this category from which 50% of people unaware of its adverse effect. To prevent its adverse effects like blindness there is an imperious need for automated technique to be developed for its detection. Recently, a genetic data has been explored with machine learning techniques for the detection and prevention of POAG. The genes along with other demographic data sets give an evident base for the detection of this disease. In this paper both the genetic and demographic data is used for the detection of this disease. Here an algorithm is proposed for preprocessing. The feature sets comprises of genes (i.e. MYOC, CYP1B1, NTF4, OPTN), SNP alleles, risk alleles, chromosomes, family history, race, age and gender of patients. For this paper, we use 590 patientsâ€™ genetic and demographic data sets from various online repositories. For the performance evaluation of the proposed approach we have applied different types of classifiers (NaÃ¯ve Bayes, J48, SMO, LWL, K*). The classifiers were evaluated to understand their ability of predicting the desire results on sensitivity, accuracy and specificity parameters. The results revealed that Support Vector Machine (SMO) classifier meet high classification accuracy i.e. 98%.
A. Youssef and A. Rich, â€œExploring trends and themes in bioinformatics literature using topic modeling and temporal analysisâ€, Proc. of the Int. Conf on Long Island Systems, Applications and Technology Conference (LISAT), IEEE, Farmingdale, NY, USA, 2018.
N.M. Luscombe, D. Greenbaum and M. Gerstein, "What is bioinformatics? A proposed definition and overview of the field", Methods Archive, vol. 40, no. 4, pp. 346-358, 2001.
K.A. Shakil and M.Aslam, â€œCloud computing in bioinformatics and big data analytics: Current status and future researchâ€, Big Data Analytics, Springer, 2018.
M. Nosheen et al. / The Nucleus 55, No. 2 (2018) 85-92
NCBI, â€œWelcome to NCBIâ€, 2018, Reterived on: 8th Feb., 2018, Url: https://www.ncbi.nlm.nih.gov.
J.S. Hamid, P.Hu, N.M. Roslin, V. Ling, C.M.T. Greenwood and J. Beyene, â€œData integration in genetics and genomics: Methods and challengesâ€, AGE Hindawi Access to Research Human Genomics and Proteomics, vol. 1, no 1, pp. 1-13, 2009.
R.A. MejÃa, C. Linares, J. Garrabou, A. Antunes, E. Ballesteros, E. Cebrian, D. David and J. Ledoux, â€œCombining genetic and demographic data for the conservation of a mediterranean marine habitat-forming speciesâ€, PLOS, pp.1-19, 2015.
R.N. Weinreb and P.P.T. Khaw, "Primary open-angle glaucomaâ€, The Lancet, vol. 363, no. 9422, pp. 1711-1720, 2004.
D. Anitha, M. Suganthi and T.S. Gnanendra, "Evaluation of data mining classifiers for prediction and classification of glaucoma associated proteins", Int. J. Pharma and Bio Science, vol. 9, no. 1, pp. 1-11, 2018.
I. Hecht,A. Achiron, V. Man and Z.Burgansky-Eliash,"Modifiable factors in the management of glaucoma: a systematic review of current evidence", Graefe's Archive for Clinical and Experimental Ophthalmology, vol. 255, no. 4, pp. 789-796, 2017.
E.M. Stone, J.H. Fingert, W.L.M. Alward, T.D. Nguyen, J.R. Polansky, S.L.F. Sunden, D. Nishimura, A.F. Clark, A. Nystuen, B.E. Nichols, D.A. Mackey, R. Ritch, J.W. Kalenak, E.R. Craven and V.C. Sheffield, "Identification of a gene that causes primary open angle glaucoma", Science, vol. 275, no. 5300, pp. 668-670, 1997.
A. Desai, D. Patel, A. Sapovadia, P. Mehta and J. Brahmbhatt, "A study of relation between primary open angle glaucoma and type II diabetes mellitus", Int. J. Res. Med. Sci., vol. 6, no. 3, pp. 997-1001, 2018.
A.M. Williams, W. Huang, K.W. Muir, S.S. Stinnett, J.S. Stone and J.A. Rosdahl, "Identifying risk factors for blindness from primary open-angle glaucoma by race: a caseâ€“control study", Clinical Ophthalmology, vol. 12, pp. 377-383, 2018.
P.W.M. Bonnemaijer, C. Cook, A. Nag, C.J. Hammond, C.M.V. Duijn, H.G. Lemij, C.C.W. Klaver and A.A.H.J. Thiadens, "Genetic African ancestry is associated with central corneal thickness and intraocular pressure in primary open-angle glaucoma", Investigative Ophthalmology & Visual Science, vol. 58, no. 7, pp. 3172-3180, 2017.
F. Wang, Y. Li, L. Lan, B. Li, L. Lin, X. Lu and J. Li, "Ser341Pro MYOC gene mutation in a family with primary open-angle glaucoma", Int. J. of Molecular Medicine, vol. 35, no. 5, pp. 1230-1236, 2015.
H.I. Elshazly, M. Waly, A.M. Elkorany and A.E. Hassanien, "Chronic eye disease diagnosis using ensemble-based classifier", Proc. of the Int. Conf. on Engg and Tech. (ICET), IEEE, pp. 1-6, 2014.
E.Oh, T.K.Yoo and S. Hong, "Artificial neural network approach for differentiating open-angle glaucoma from glaucoma suspect without a visual field test", Investigative Ophthalmology & Visual Science, vol. 56, no. 6, pp. 3957-3966, 2015.
T.R. Kausu,V.P. Gopi, K.A. Wahid, W. Domaand S.I. Niwas, "Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images", Biocybernetics and Biomedical Engineering, vol. 38, no. 2, pp. 329-341, 2018.
S.J. Kim, K.J. Cho and S.Oh, "Development of machine learning models for diagnosis of glaucoma", PloS One, vol. 12, No. 5, pp. 1-16, 2017.
E. Long, P.Wan and Y. Zhuo, "Predicting the real-world future of glaucoma patients? Cautions are required for machine learning", Translational Vision Science & Technology, vol. 6, no. 6, pp. 1-2, 2017.
K.N. Rao, S. Nagireddy and S. Chakrabarti, "Complex genetic mechanisms in glaucoma: an overview" Indian Journal of Ophthalmology, vol. 59, no. Suppl 1, pp. 31-42, 2011.
N.A. Restrepo, E. Farber-Eger, R. Goodloe, J.L. Haines and D.C. Crawford, "Extracting primary open-angle glaucoma from electronic medical records for genetic association studies", PloS one, vol. 10, no. 6, pp. 1-15, 2015.
H.I. Elshazly, M. Waly, A.M. Elkorany and A.E. Hassanien, â€œChronic eye disease diagnosis using ensemble-based classifierâ€, Int. Conf. on Engg. and Tech. (ICET), IEEE, pp. 1-6, 2014.
M.N. Rao, M. Rao and V. Gopala, "A fusion technique to classify glaucoma from fundus images", IIOAB JOURNAL, vol. 7, no. 9, pp. 812-824, 2016.
K.N. Rao, I. Kaur, R.S. Parikh, A.K. Mandal, G. Chandrasekhar, R. Thomas and S. Chakrabarti, "Variations in NTF4, VAV2 and VAV3 genes are not involved with primary open-angle and primary angle-closure glaucomas in an indian population", Investigative Ophthalmology & Visual Science, vol. 51, no. 10, pp. 4937-4941, 2010.
Oracle, "Oracle advanced analytics' machine learning algorithms sql functions", 2018, Reterived on: 8-Feb-2018, Url: http://www.oracle. com/technetwork/database/enterprise-edition/odm-techniques-algorithms-097163.html.
S. Kumar, M.A. Malik, K. Sooraj, R. Sihota and J. Kaur, "Genetic variants associated with primary open angle glaucoma in Indian population", Genomics, vol. 109, no. 1, pp. 27-35, 2017.
C.M. Green, L.S. Kearns, J. Wu, J.M. Barbour, R.M. Wilkinson, A. Maree, T.L. Wong, A.W. Hewitt and D.A. Mackey, "How significant is a family history of glaucoma? Experience from the Glaucoma Inheritance Study in Tasmania", Clinical & Experimental Ophthalmology, vol. 35, no. 9, pp. 793-799, 2007.
B.T. Whigham, S.E.I. Williams, Y. Liu, R.M. Rautenbach, T.R. Carmichael, J. Wheeler, A. Ziskind, X.Qin, S. Schmidt, M. Ramsay, M. A.Hauser and R.R. Allingham, "Myocilin mutations in black South Africans with POAG", Molecular Vision, vol. 17, no. 1, pp. 1064-1069, 2011.
F.S. Philomenadin, R. Asokan, N. Viswanathan, R. George, V. Lingam and S. Sarangapani, "Genetic association of SNPs near ATOH7, CARD10, CDKN2B, CDC7 and SIX1/SIX6 with the endophenotypes of primary open angle glaucoma in Indian population", PloS one, vol. 10, no. 3, pp. 1-12, 2015.
V. Avagyan, N. Ma, J. Papadopoulos , K. Bealer and T.L. Madden, â€œBLAST+: architecture and applications",Bioinformatics, vol. 10, no. 421, pp. 1-9, 2009.
DAVID, â€œDAVID Bioinformatics Resourcesâ€, 2018, Reterived on 20th Feb 2018, URL: https://david.ncifcrf.gov.
J.Ostell, â€œThe Entrez Search and Retrieval Systemâ€, NCBI, 2018.
M. Safran, I. Dalah, J. Alexander, N.Rosen, T.I. Stein, M. Shmoish, N. Nativ, I. Bahir,T. Doniger, H. Krug, A. Sirota-Madi, T. Olender,Y.Golan,G. Stelzer, A. Harel and D. Lancet, â€œGeneCards Version 3: The human gene integratorâ€, The Journal of Biological Database and Curration, vol. 1, pp. 1-16, 2010.
A. Daemen, O.Gevaert and B.D.Moor, â€œIntegration of clinical and microarray data with kernel methodsâ€, Proc. of the 29th Annual International Conference of the EMBS, IEEE, Lyon, France, 2007.
O.G. Troyanskaya, K. Dolinski, A.B. Owen, R.B. Altman and D. Botstein, â€œA Bayesian framework for combining heterogeneous data sources for gene function predictionâ€, Proc. of the National Academy of Sciences of the United States of America, vol. 100, no. 14, pp.8349-8353, 2003.
A. Abdiansah and R. Wardoyo, â€œTime Complexity Analysis of Support Vector Machines (SVM) in LibSVMâ€, Int. J. Comp. Appl., vol. 128, no. 3, pp. 0975 â€“ 8887, 2005.
P. Kumar, S.Henihoff and C.P.Ng, â€œPredicting the effects of coding non- synonymous variants on protein fucntion using the SIFT algorithmâ€, Nature Protocols, vol. 4, no. 8, pp. 1073-1082, 2009.
O. Gevaert, F.D. Smet, D. Timmerman, Y.Moreau and B.D. Moor, â€œPredicting the prognosis of brest cancer by integrating clinical and microarray data with Bayesian Networksâ€, Bioinformatics, vol 22, no. 14, pp.185-190, 2006.
M. Nosheen et al. / The Nucleus 55, No. 2 (2018) 85-92
R.P. Aharwal, â€œEvaluation of various classification techniques of weka using different datasetsâ€, Int. J. Adv. Res. and Innov. Ideas in Education, vol. 2, no. 2, pp. 2395-4396 , 2016.
A. Hoover and M. Goldbaum, "Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels", IEEE transactions on medical imaging, vol. 22, no. 8, pp. 951-958, 2003.
Z. Zhang, J. Liu, C.K. Kwoh, X. Sim, W.T. Tay, Y. Tan and F. Yin, "Learning in glaucoma genetic risk assessment", Proc. of Annual Int. Conference of the IEEE on Engg. in Med. and Bio. Soc. (EMBC), pp. 6182-6185, 2010.
Y. Tokuda, T. Yagi, K. Yoshii, Y. Ikeda, M. Fuwa, M. Ueno, M. Nakano, N. Omi, M. Tanaka, K. Mori, M. Kageyama, I. Nagasaki, K. Yagi, S. Kinoshita and K. Tashir, "An approach to predict the risk of glaucoma development by integrating different attribute data", SpringerPlus, vol. 1, no. 1, pp. 1-10, 2012.
M. Eng, A.S. Waly and K.Wahba, "A comparison of different prediction models in the progression of ocular hypertension to primary open angle glaucoma", Int. J. Appl. Inform. Sys., vol. 5, no. 3, pp. 30-41, 2013.
J.H. Moore, C.S. Greene and D.P. Hill, â€œIdentification of novel genetic models of glaucoma using the â€œEMERGENTâ€ genetic programming-Based artificial intelligence system, Genetic Programming Theory and Practice XII, Springer, pp. 17-35, 2015.
A. Agarwal, S. Gulia, S. Chaudhary, M. K. Dutta, R. Burget and K. Riha, "Automatic glaucoma detection using adaptive threshold based technique in fundus image", Proc. of the 38th Int. Conf. on Telecommunications and Signal Processing (TSP), IEEE, pp. 416-420,2015.
C. Agurto, S. Nemeth, G. Zamora, M. Vahtel, P. Soliz and S. Barriga, "Comprehensive eye evaluation algorithm", Proc. of the Medical Imaging 2016: Computer-Aided Diagnosis, Int. Society for Optics and Photonics, pp. 978518-7, 2016.
C. Huang, L. Xie, Z. Wu, Y. Cao, Y. Zheng, C. Pang and M. Zhang, "Detection of mutations in MYOC, OPTN, NTF4, WDR36 and CYP1B1 in Chinese juvenile onset open-angle glaucoma using exome sequencing", Scientific reports, vol. 8, no. 1, pp. 1-8, 2018.
N.A. Apreutesei, F. Tircoveanu, A. Cantemir, C. Bogdanici, C. Lisa, S. Curteanu and D. Chiselita, "Predictions of ocular changes caused by diabetes in glaucoma patients", Computer methods and programs in biomedicine, vol. 154, pp183-190, 2018.
S. Baboolal and D. Smit "South African Eye Study (SAES): Ethnic differences in central corneal thickness and intraocular pressure", Eye, 2018.
P. Gharahkhani, K.P. Burdon, J.N.C. Bailey, A.W. Hewitt, M.H. Law, Louis. R. Pasquale, J.H. Kang, J.L. Haines, E. Souzeau, T. Zhou, O.M. Siggs, J. Landers, M. Awadalla, S. Sharma, R.A. Mills, B. Ridge, D. Lynn, R. Casson, S.L. Graham, I. Goldberg, A. White, P.R. Healey, J. Grigg, M. Lawlor, P. Mitchell, J. Ruddle, M. Coote, M. Walland, S. Best, A. Vincent, J. Gale, G. RadfordSmith, D.C. Whiteman, G. W. Montgomery, N.G. Martin, D.A. Mackey, J.L. Wiggs, S. MacGregor and J.E. Craig, "Analysis combining correlated glaucoma traits identifies five new risk loci for open-angle glaucoma", Scientific Reports, vol. 8, no. 1, pp. 1-12, 2018.
K.S. Biggerstaff, B.J. Frankfort, S. Orengo-Nania, J. Garcia, E. Chiao, J.R. Kramer and D. White, "Validity of code based algorithms to identify primary open angle glaucoma (POAG) in Veterans Affairs (VA) administrative databases", Ophthalmic epidemiology, vol. 25, no. 2, pp. 162-168, 2018.
K. Nitta, R. Wajima, G. Tachibana, S. Inoue, T. Ohigashi, N. Otsuka, H. Kurashima, K. Santo, M. Hashimoto, H. Shibahara, M. Hirukawa and K. Sugiyama, "Prediction of Visual Field Progression in Patients with Primary Open-Angle Glaucoma, Mainly Including Normal Tension Glaucoma", Scientific Reports, vol. 7, no. 1, pp. 1-12, 2017.