Breast cancer is known as the most common cancer among women so that in 2012, 29% of cases were diagnosed among
women have been infected with breast cancer. Early diagnosis of breast cancer (max. 5 years after the first cell division of the cancer) the
patient's chance of survival increases from 56% to over 86% .Therefore existence of a precise and reliable system for timely diagnosis of
benign and malignant breast tumors is very important. A lot of details about cancer characteristics make diagnosis difficult for doctors,
Therefore, data analysis methodologies will be a useful assistant for doctors to diagnose cancer. Currently, using FNA as a method for
tumor mass sampling and testing on that type of tumor (benign and malignant) is indicated. By performing data mining algorithms on the
data obtained from the sampling, a higher accuracy can be detected. The data set used in this study was extracted from the data set in the
machine learning tank of university of California known as UCI. In this thesis we want to benign or malignant breast cancer detection
used from new method iLA-VQIS (combination of two competitive algorithm : LVQ and evolutionary immune system algorithm) to
improve detection. In fact, the training of neural network weights is done using an artificial immunological clonal algorithm. Inside this
function, instead of gradients based on the neural network, evolutionary optimization method will be used and usually the function
arguments will not be changed. Point of subscription between artificial immune system evolutionary optimization algorithms with the
neural networks topic, after designing the network structure is the learning process that ends with an optimization problem and finally the
results are evaluated with three criteria for precision, accuracy and recall. Simulation operations performed in MATLAB and results of
the proposed LA-VQIS algorithm has been compared with basic algorithms such as Kohenen LVQ algorithm, combined decision tree
algorithm with genetic algorithm, K_SWM algorithm, SWM and MSAIS algorithm under the same conditions and based on the
correctness and accuracy of the diagnosis.
Published In:IJCSN Journal Volume 6, Issue 5
Date of Publication : October2017
Pages : 539-546
Tables : 06
Esmat Banihashem : Collegian of Master of Science in Artificial Intelligence, Electronic Unit of Azad University
Tehran, Ir Iran.
Touraj Banirostam : Department of Computer Engineering, Islamic Azad University,
Central Tehran Branch, Tehran, Iran.
breast cancer, data mining, LVQ neural network, artificial immune system
In the current research, we focus on the special application
of synthetic immune algorithm. In fact, we investigated the
data mining process through the synthetic immune
function. The main part of this research is investigating the
neural network education by means of the syntactic
immune algorithm. In other words, by the supervising the
syntactic immune program in the Train neural network, the
educated data transforms to the antibody environment and
the remained data are transformed to the antigen
environment. Finally, three criteria would be evaluated as
the following: accuracy, supervise and precision. Totally,
the results of evaluated simulation A-VQIS with 97.8%
accuracy illustrate the effect of LA-VQIS method on the
data in order to diagnosis the breast cancer. Thus, we
implemented the knowledge of a doctor as the smart
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