Abstract
This paper presents a novel approach for complex disease prediction that we have developed, exemplified by a study on risk of coronary artery disease (CAD). This multi-disciplinary approach straddles fields of microarray technology and genetics, neural networks (NN), data mining and machine learning, as well as traditional statistical analysis techniques, namely principal components analysis (PCA) and factor analysis (FA). A description of the biological background of the study is given, followed by a detailed description of how the problem has been modeled for analyses by neural networks and FA. A committee learning approach for NN has been used to improve generalization rates. We show that our NN approach is able to yield promising prediction results despite using only the most fundamental network structures. More interestingly, through the statistical analysis process, genes of similar biological functions have been clustered. In addition, a gene marker involved in breaking down lipids has been found to be the most correlated to CAD.
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