Coronary Artery Disease Prediction Using Dna Microarrays, Neural Networks and Other Statistical Analysis Tools

This paper aims to illustrate a novel approach of complex disease prediction, exemplified by a coronary artery disease (CAD) study that we have developed. This multidisciplinary 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 have been used to improve generalisation rates. It has been shown that our NN approach was 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.