A Prediction Model for Complex Diseases using Set Association & Artificial Neural Network


KIPS Transactions on Computer and Communication Systems, Vol. 15, No. 4, pp. 323-330, Aug. 2008
10.3745/KIPSTB.2008.15.4.323,   PDF Download:

Abstract

Since complex diseases are caused by interactions of multiple genes, traditional statistical methods are limited in its power to predict the onset of a complex disease. Recently new approaches using machine learning techniques are introduced. Neural nets are a suitable model to find patterns in complex data. When large amount of data are fed into a neural net, however, it takes a long time for learning and finding patterns. In this study we suggest a new model that combines the set association, which is a statistical technique to find important SNPs associated with complex diseases, and neural network. We experiment with SNP data related to asthma to test the effectiveness of our model. Our model shows higher prediction accuracy and shorter execution time than neural net only. We expect our model can be used effectively to predict the onset of other complex diseases.


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Cite this article
[IEEE Style]
H. J. Choi, S. H. Kim and K. B. Wee, "A Prediction Model for Complex Diseases using Set Association & Artificial Neural Network," KIPS Journal B (2001 ~ 2012) , vol. 15, no. 4, pp. 323-330, 2008. DOI: 10.3745/KIPSTB.2008.15.4.323.

[ACM Style]
Hyun Joo Choi, Seung Hyun Kim, and Kyu Bum Wee. 2008. A Prediction Model for Complex Diseases using Set Association & Artificial Neural Network. KIPS Journal B (2001 ~ 2012) , 15, 4, (2008), 323-330. DOI: 10.3745/KIPSTB.2008.15.4.323.