Machine Learning Method to Establish the Connection Between Age Related Macular Degeneration and Some Genetic Variations

Medicine research based in machine learning methods allows the improvement of diagnosis in complex diseases. Age related Macular Degeneration (AMD) is one of them. AMD is the leading cause of blindness in the world. It causes the 8.7 % of blind people. A set of case and controls study could be developed by machine-learning methods to find the relation between Single Nucleotide Polymorphisms (SNPs) SNP_A, SNP_B, SNP_C and AMD. In this paper we present a machine-learning based analysis to determine the relation of three single nucleotide SNPs and the AMD disease. The SNPs SNP_B, SNP_C remained in the top four relevant features with ophthalmologic surgeries and bilateral cataract. We aim also to determine the best set of features for the classification process.

[1]  H. Brodaty,et al.  Psychosocial effects of age-related macular degeneration , 2006, International Psychogeriatrics.

[2]  Johanna M Seddon,et al.  Risk factors for the incidence of Advanced Age-Related Macular Degeneration in the Age-Related Eye Disease Study (AREDS) AREDS report no. 19. , 2003, Ophthalmology.

[3]  J. Machado,et al.  Abstract Computation in Schizophrenia Detection through Artificial Neural Network Based Systems , 2015, TheScientificWorldJournal.

[4]  Abhijit Dasgupta,et al.  Brief review of regression‐based and machine learning methods in genetic epidemiology: the Genetic Analysis Workshop 17 experience , 2011, Genetic epidemiology.

[5]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[6]  P. Donnelly,et al.  Genome-wide strategies for detecting multiple loci that influence complex diseases , 2005, Nature Genetics.

[7]  Rajiv Raman,et al.  A 32 kb Critical Region Excluding Y402H in CFH Mediates Risk for Age-Related Macular Degeneration , 2011, PloS one.

[8]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[9]  G. Murthy,et al.  Prevalence of early and late age-related macular degeneration in a rural population in northern India: the INDEYE feasibility study. , 2007, Investigative ophthalmology & visual science.

[10]  T. Das,et al.  Risk factors for age-related macular degeneration: findings from the Andhra Pradesh eye disease study in South India. , 2005, Investigative ophthalmology & visual science.

[11]  R. Klein,et al.  Causes and prevalence of visual impairment among adults in the United States. , 2004, Archives of ophthalmology.

[12]  P. Jong.  Prevalence of age-related macular degeneration in the United States. , 2004 .

[13]  J. Zenteno,et al.  Susceptibility to advanced age-related macular degeneration and alleles of complement factor H, complement factor B, complement component 2, complement component 3, and age-related maculopathy susceptibility 2 genes in a Mexican population , 2012, Molecular vision.

[14]  J. Pacheco,et al.  A Validated Phenotyping Algorithm for Genetic Association Studies in Age-related Macular Degeneration , 2015, Scientific Reports.

[15]  Eleazar Eskin,et al.  Postassociation cleaning using linkage disequilibrium information , 2011, Genetic epidemiology.

[16]  R. Kim,et al.  Prevalence of vitreoretinal disorders in a rural population of southern India: the Aravind Comprehensive Eye Study. , 2004, Archives of ophthalmology.

[17]  G. Hageman,et al.  Age-Related Macular Degeneration (AMD) , 2008 .

[18]  N. V. Chong,et al.  Age-related macular degeneration: a perspective on genetic studies , 2008, Eye.

[19]  R. Klein,et al.  Inflammation, complement factor h, and age-related macular degeneration: the Multi-ethnic Study of Atherosclerosis. , 2008, Ophthalmology.

[20]  Mauro Giacomini,et al.  Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach , 2015, BMC Ophthalmology.

[21]  Gary C. Brown,et al.  Analysis of six genetic risk factors highly associated with AMD in the region surrounding ARMS2 and HTRA1 on chromosome 10, region q26. , 2010, Investigative ophthalmology & visual science.

[22]  Kurt Miller,et al.  Artificial neural networks and prostate cancer—tools for diagnosis and management , 2013, Nature Reviews Urology.

[23]  A. K. Mousa,et al.  Comment on “On Soft β-Open Sets and Soft β-Continuous Functions” , 2015, TheScientificWorldJournal.

[24]  B. Rovner,et al.  Effect of depression on vision function in age-related macular degeneration. , 2002, Archives of ophthalmology.

[25]  Johanna M Seddon,et al.  Evaluation of the clinical age-related maculopathy staging system. , 2006, Ophthalmology.

[26]  Dimitrios I. Fotiadis,et al.  Machine learning applications in cancer prognosis and prediction , 2014, Computational and structural biotechnology journal.

[27]  F. Ferris,et al.  Blindness and visual impairment: a public health issue for the future as well as today. , 2004, Archives of ophthalmology.