Classification of breast cancer data with harmony search and back propagation based artificial neural network

Today, breast cancer is one of the most frequently seen cancer types. Analyses of breast cells characteristics have great importance in diagnosis, treatment and following of this disease. In this study, classification of 699 instances of breast cancer data that is available in UCI is performed through two different types of artificial neural network algorithm. In the literature, numerous algorithms are used for training of artificial neural network. In this study, harmony search and back propagation algorithms are used to train feed forward artificial neural network. Performance values of classification are found by means of Accuracy/SSE/Regression parameters. The performance values of back propagation are obtained as 94.1/0.007/0.92 while the results obtained by the harmony search algorithm are 97.57/0.005/0.96 respectively. In the first time with the study, breast cancer data are classified with harmony search based artificial neural network algorithm and high performance values are obtained.