Improved wavelet neural network for early diagnosis of cancer patients using microarray gene expression data

In clinical practice, diagnostic dilemmas are frequently encountered in discriminating the heterogeneous cancers into distinct types. This paper reports an improved machine learning approach based on the wavelet neural network (WNN), which associates a feature selection method, namely, the conditional T-test. It is used in the development of cancer classification by using benchmark microarray data. The experimental results showed that the proposed classifiers achieved a superior accuracy, which ranges from 92% to 100%. Performance comparisons are also made with other classifiers which show that this proposed approach outperforms most of them.