Journal of Computer Engineering & Information TechnologyISSN : 2324-9307

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Research Article, J Comput Eng Inf Technol Vol: 3 Issue: 2

Artificial Immune Systems with Negative Selection Applied To Clinical Diagnosis of Breast Cancer Samples

Fernando PA Lima1*, Anna Diva P Lotufo1, Carlos R Minussi1, Mara LM Lopes2
1Electrical Engineering Department, Intelligent Systems Laboratory, Universidade Estadual Paulista, Brazil.
2Mathematics Department, Intelligent Systems Laboratory, Universidade Estadual Paulista, Brazil.
Corresponding author : Fernando PA Lima
Department of Electrical Engineering, Universidade Estadual Paulista, Brazil
E-mail: [email protected]
Received: April 07, 2014 Accepted: June 19, 2014 Published: June 25, 2014
Citation: Lima FPA, Lotufo ADP, Minussi CR, Lopes MLM (2014) Artificial Immune Systems with Negative Selection Applied To Clinical Diagnosis of Breast Cancer Samples. J Comput Eng Inf Technol 3:1. doi:10.4172/2324-9307.1000118

Abstract

Artificial Immune Systems with Negative Selection Applied To Clinical Diagnosis of Breast Cancer Samples

This paper uses an artificial immune systems with negative selection applied for diagnosing breast cancer samples. Taking as basis a biological process, the negative selection principle. This process is used to discriminate the samples, attaining a classification for benign or malignant cases. The main application of the method is assist professionals in the breast cancer diagnostic process, thereby providing decision-making agility, efficient treatment planning, reliability and the necessary intervention to save lives. To evaluate this method, the Wisconsin Breast Cancer Diagnosis database was used. This is an actual breast cancer database. The results obtained using the method (99.77% of accuracy in the better configuration of methodology), when compared with the specialized literature, show accuracy, robustness and efficiency in the breast cancer diagnostic process.

Keywords: Breast Cancer Samples; Clinical Diagnosis; Artificial Immune Systems; Negative Selection Algorithm.

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