Incremental Tumor Diagnosis Algorithm Using New Unlabeled Microarray

This paper presents an implementation of incremental tumor diagnosis algorithm (ITDA) on microarray data for improving diagnostic accuracy of tumor. A classifier (BP or KNN) was used in the algorithm to estimate confidences of a new unlabeled sample in different classes. When one confidence is higher than the threshold, the sample will be labeled; otherwise, the sample will be diagnosed by medical expert with other approaches. With more and more new labeled samples adding in the labeled samples list, the classifier will be better and will provide more accurate diagnosis for new sample. Strictly speaking, it is a particular active learning algorithm. By applying this algorithm on Colon tumor dataset, we demonstrated that our incremental tumor diagnosis algorithm can be used to successfully improve diagnostic accuracy of tumor. The performance of different parameters was tested in our experiments to verify the applicability of the method.