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Improving breast cancer prediction using a pattern recognition network with optimal feature subsets
1Serdar Gündoğdu, Department of Computer Technologies, Bergama Vocational School, Dokuz Eylül University, 35700 Bergama, Izmir, Turkey, serdar.gundogdu@deu.edu.tr.
Aim:
To predict the presence of breast cancer by using a pattern recognition network with optimal features based on routine blood analysis parameters and anthropometric data.
Methods:
Sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), and Fowlkes-Mallows (FM) index of each model were calculated. Glucose, insulin, age, homeostatic model assessment, leptin, body mass index (BMI), resistin, adiponectin, and monocyte chemoattractant protein-1 were used as predictors.
Results:
Pattern recognition network distinguished patients with breast cancer disease from healthy people. The best classification performance was obtained by using BMI, age, glucose, resistin, and adiponectin, and in a model with two hidden layers with 11 and 100 neurons in the neural network. The accuracy, sensitivity, specificty, FM index, and MCC values of the best model were 94.1%, 100%, 88.9%, 94.3%, and 88.9%, respectively.
Conclusion:
Breast cancer diagnosis was succesfully predicted using only five features. A model using a pattern recognition network with optimal feature subsets proposed in this study could be used to improve the early detection of breast cancer.
