Self-Organizing Map Based Data Detection of Hematopoietic Tumors

Akitsugu OHTSUKA  Hirotsugu TANII  Naotake KAMIURA  Teijiro ISOKAWA  Nobuyuki MATSUI  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E90-A   No.6   pp.1170-1179
Publication Date: 2007/06/01
Online ISSN: 1745-1337
DOI: 10.1093/ietfec/e90-a.6.1170
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Nonlinear Problems
hematopoietic tumor,  self-organizing map,  immune algorithm,  genetic algorithm,  

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Data detection based on self organizing maps is presented for hematopoietic tumor patients. Learning data for the maps are generated from the screening data of examinees. The incomplete screening data without some item values is then supplemented by substituting averaged non-missing item values. In addition, redundant items, which are common to all the data and tend to have an unfavorable influence on data detection, are eliminated by a genetic algorithm and/or an immune algorithm. It is basically judged, by observing the label of a winner neuron in the map, whether the data presented to the map belongs to the class of hematopoietic tumors. Some experimental results are provided to show that the proposed methods achieve the high probability of correctly identifying examinees as hematopoietic tumor patients.