A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breastcancer

Authors:
R. Karakış;M. Tez;Y. A. KıLıç;Y. Kuru;İ. GüLer
Affiliations:
Department of Computer and Electronics Technology, Faculty of Technology, Gazi University, 06500 Teknikokullar, Ankara, Turkey;Department of Surgery, Ankara Numune Educational and Research Hospital, Ankara, Turkey;Department of Surgery, Hacettepe University Faculty of Medicine, Ankara, Turkey;Department of Surgery, Ankara Oncology Hospital, Ankara, Turkey;Department of Computer and Electronics Technology, Faculty of Technology, Gazi University, 06500 Teknikokullar, Ankara, Turkey
Venue:
Engineering Applications of Artificial Intelligence
Year:
2013

Citing 9
Cited 1

Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Optimizing feedforward artificial neural network architecture

Engineering Applications of Artificial Intelligence
A simulated-annealing-based approach for simultaneous parameter optimization and feature selection of back-propagation networks

Expert Systems with Applications: An International Journal
Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach

Artificial Intelligence in Medicine
Review: Neural networks and statistical techniques: A review of applications

Expert Systems with Applications: An International Journal
Comparing the performance of neural networks developed by using Levenberg-Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process

Expert Systems with Applications: An International Journal
Application and performance analysis of neural networks for decision support in conceptual design

Expert Systems with Applications: An International Journal
A fuzzy logic based-method for prognostic decision making in breast and prostate cancers

IEEE Transactions on Information Technology in Biomedicine
Mutation-based genetic neural network

IEEE Transactions on Neural Networks

MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Axillary Lymph Node (ALN) status is an extremely important factor to assess metastatic breast cancer. Surgical operations which may be necessary and cause some adverse effects are performed in determination ALN status. The purpose of this study is to predict ALN status by means of selecting breast cancer patient's basic clinical and histological feature(s)that can be obtained in each hospital. 270 breast cancer patients' data are collected from Ankara Numune Educational and Research Hospital and Ankara Oncology Educational and Research Hospital. These are classified using back propagation MultiLayer Perceptron (MLP), Logistic Regression (LR) and Genetic Algorithm (GA) based MLP models. Receiver Operating Characteristics (ROC) such as sensitivity, specificity, accuracy and area under of ROC (AUC) and regression are used to evaluate performances of the developed models. It is concluded from LR and GA based MLP, that menopause status and lymphatic invasion are the most significant features for determining ALN status. GA provides to select best features as MLP inputs. It also optimizes the weights of backpropagation algorithm in MLP. The values of regression and accuracy of the GA based MLP with 9features (numerical age, categorical age, menopause status, tumor size, tumor type, tumor location, T staging, tumor grade and lymphatic invasion) are found as 0.96 and 98.0% with respectively. According to results, proposed GA based MLP classifier can be used to predict the ALN status of breast cancer without surgical operations.