Volume 25, Issue 130 (11-2015)                   J Mazandaran Univ Med Sci 2015, 25(130): 100-115 | Back to browse issues page

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Daaraaee M, Vahidi J, Alipour A. A Method Based on an Evolutionary Algorithm to Achieve an Efficient Artificial Neural Network Model for Prediction of Breast Tumors Status. J Mazandaran Univ Med Sci. 2015; 25 (130) :100-115
URL: http://jmums.mazums.ac.ir/article-1-6406-en.html
Abstract:   (4439 Views)

Background and purpose: Intelligent methods such as artificial neural networks (ANN) have been recently used as an efficient model for prediction and classification of tumors. Diagnosis of benign and malignant breast tumors based on morphological, clinical and demographic features without using invasive paraclinical methods is very important. The aim of this study was to provide a neural network model to predict the status of breast tumors and compare its efficacy with the common regression model.

Materials and methods: In this study, Wisconsin breast cancer database was used. It was obtained from cytology results of the breast tumors of 683 patients. In the proposed model different features such as clump thickness, uniformity of cell size, uniformity of cell shape, etc. were used as input variables. We applied the genetic algorithm (GA) for determination of the best structure and training of multi-layer NN model was implemented in MATLAB. The performance of proposed NN model was compared appling logistic regression (LR) in SPSS. 5-fold cross validation was used for accurate calculation of the performance of the models.

Results: The results found GA capable of determining the best structure for a multi-layer NN and train it properly. In different performances the best NN structure was NN(9-8-6-1) with an average accuracy, sensitivity, specificity, and AUC (area under ROC curve) of 0.971, 0.988, 0.962, and 0.9955, respectively, while the values of the corresponding parameters for LR were 0.968, 0.975, 0.964 and 0.9954, respectively.

Conclusion: The achieved ANN model could be used as a method with high sensitivity and specificity alongside common non-invasive diagnostic methods as a diagnosis support system to identify benign and malignant breast tumors.

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Type of Study: Research(Original) |

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