New cancer diagnosis modeling using boosting and projective adaptive resonance theory with improved reliable index

Author(s): Honda, H. | Kobayashi, T. | Takahashi, H. | Murase, Y. |

Year: 2007

Citation: BIOCHEMICAL ENGINEERING JOURNAL Volume: 33 Issue: 2 Pages: 100-109

Abstract: An optimal and individualized treatment protocol based on accurate diagnosis is urgently required for the adequate treatment of patients. For this purpose, it is important to develop a sophisticated algorithm that can manage large amount of data, such as gene expression data from DNA microarray, for optimal and individualized diagnosis. Especially, marker gene selection is essential in the analysis of gene expression data. In the present study; we developed the combination method of projective adaptive resonance theory and boosted fuzzy classifier with SWEEP operator method for model construction and marker selection. And we applied this method to microarray data of acute leukemia and brain tumor. The method enabled the selection of 14 important genes related to the prognosis of the tumor. In addition, we proposed improved reliability index for cancer diagnostic prediction of blinded subjects. Based on the index, the discriminated group with over 90% prediction accuracy was separated from the others. PART-BFCS with improved RIBFCS method does not only show high performance, but also has the feature of reliable prediction further. This result suggests that PART-BFCS with improved RIBFCS method has the potential to function as a new method of class prediction for diagnosis of patients.

Topics: Machine Learning, Applications: Medical Diagnosis, Models: ARTMAP,

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