Autoencoder networks for HIV classification

1in 1982 to describe the first cases of unusual immune system failure that were identified in the previous year. The human immunodeficiency virus (HIV) was later identified as the cause of AIDS. Risk factor epidemiology examines the individual demographic and social characteristics and attempts to determine factors that place an individual at risk of acquiring a life-threatening disease 2 . In this study, the demographic and social characteristics of the individuals and their behaviour are used to determine the risk of HIV infection; referred to as biomedical individualism 2,3 . By identifying the individual risk factors that lead to the HIV infection, it is possible to modify social conditions, which give rise to the disease, and thus design effective HIV prevention policies 2 . A model will be created and used to classify the HIV status of individuals based on demographic properties. In this study, the model is created using autoencoder neural networks and genetic algorithms, which have been applied to classification. An artificial neural network ( ANN) is an inter-connected structure of processing elements. The ANN structure 4

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[4]  M. Kramer.  Nonlinear principal component analysis using autoassociative neural networks , 1991 .

[5]  W. T. Illingworth,et al.  Practical guide to neural nets , 1991 .

[6]  N. Krieger,et al.  Understanding AIDS: historical interpretations and the limits of biomedical individualism. , 1993, American journal of public health.

[7]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[8]  A Kartashov,et al.  Quality and efficiency of retrieval for Willshaw-like autoassociative networks. II. Recognition , 1995 .

[9]  Mauro J. Atalla,et al.  Model updating using neural networks , 1996 .

[10]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[11]  L Ohno-Machado,et al.  Sequential use of neural networks for survival prediction in AIDS. , 1996, Proceedings : a conference of the American Medical Informatics Association. AMIA Fall Symposium.

[12]  Daniel Svozil,et al.  Introduction to multi-layer feed-forward neural networks , 1997 .

[13]  Robert E. Uhrig,et al.  Use of Autoassociative Neural Networks for Signal Validation , 1998, J. Intell. Robotic Syst..

[14]  Donna L. Hudson,et al.  Neural networks and artificial intelligence for biomedical engineering , 1999 .

[15]  E. Laumann,et al.  Racial/ethnic group differences in the prevalence of sexually transmitted diseases in the United States: a network explanation. , 1999, Sexually transmitted diseases.

[16]  Frank L. Lewis,et al.  Introduction to the special issue on neural network feedback control , 2001, Autom..

[17]  Guido Bugmann,et al.  NEURAL NETWORK DESIGN FOR ENGINEERING APPLICATIONS , 2001 .

[18]  Chang W. Lee,et al.  Assessment of HIV/AIDS-related health performance using an artificial neural network , 2001, Inf. Manag..

[19]  Tshilidzi Marwala.  Probabilistic Fault Identification Using a Committee of Neural Networks and Vibration Data , 2001 .

[20]  Ian T. Nabney,et al.  Netlab: Algorithms for Pattern Recognition , 2002 .

[21]  Paulo J. G. Lisboa,et al.  A review of evidence of health benefit from artificial neural networks in medical intervention , 2002, Neural Networks.

[22]  D Sardari,et al.  Applications of artificial neural network in AIDS research and therapy. , 2002, Current pharmaceutical design.

[23]  Pong-Jeu Lu,et al.  Application of Autoassociative Neural Network on Gas-Path Sensor Data Validation , 2002 .

[24]  Nejib Smaoui,et al.  Analyzing the Dynamics of Cellular Flames Using Karhunen-Loève Decomposition and Autoassociative Neural Networks , 2002, SIAM J. Sci. Comput..

[25]  Makarand Deo,et al.  Prediction of breaking waves with neural networks , 2003 .

[26]  Lucila Ohno-Machado,et al.  A neural network-based similarity index for clustering DNA microarray data , 2003, Comput. Biol. Medicine.

[27]  D. Celentano,et al.  The social epidemiology of human immunodeficiency virus/acquired immunodeficiency syndrome. , 2004, Epidemiologic reviews.

[28]  Ahmet Alkan,et al.  Automatic seizure detection in EEG using logistic regression and artificial neural network , 2005, Journal of Neuroscience Methods.

[29]  A. Smoleń,et al.  Artificial neural network computer prediction of ovarian malignancy in women with adnexal masses , 2005, International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics.

[30]  Ah-Hwee Tan,et al.  Predictive neural networks for gene expression data analysis , 2005, Neural Networks.

[31]  T. Marwala,et al.  Fault classification in structures with incomplete measured data using autoassociative neural networks and genetic algorithm , 2006 .

[32]  Julio Caballero,et al.  Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks. , 2006, Bioorganic & medicinal chemistry.

[33]  Reeti Tandon,et al.  Neural networks for longitudinal studies in Alzheimer's disease , 2006, Artif. Intell. Medicine.