Assisting Cancer Diagnosis with Fuzzy Neural Networks

Abstract

Cancer diagnosis from huge microarray gene expression data is an important and challenging bioinformatics research topic. We used a fuzzy neural network (FNN) proposed earlier for cancer classification. This FNN contains three valuable aspects i.e., automatically generating fuzzy membership functions, parameter optimization, and rule-base simplification. One major obstacle in microarray data set classifier is that the number of features (genes) is much larger than the number of objects. We therefore used a feature selection method based on t-test to select more significant genes before applying the FNN. In this work we used three well-known microarray databases, i.e., the lymphoma data set, the small round blue cell tumor (SRBCT) data set, and the ovarian cancer data set. In all cases we obtained 100% accuracy with fewer genes in comparison with previously published results. Our result shows the FNN classifier not only improves the accuracy of cancer classification problem but also helps biologists to find a better relationship between important genes and development of cancers.