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


KARAKIŞ R. , Tez M., KILIÇ Y. A. , Kuru Y., Guler I.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, cilt.26, sa.3, ss.945-950, 2013 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 26 Konu: 3
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1016/j.engappai.2012.10.013
  • Dergi Adı: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
  • Sayfa Sayıları: ss.945-950

Özet

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 9 features (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. (c) 2012 Elsevier Ltd. All rights reserved.