Advancement in information and technology has made a major impact on medical science where the researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer is one such disease killing large... more
Advancement in information and technology has made a major impact on medical science where the researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer is one such disease killing large number of people around the world. Diagnosing the disease at its earliest instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases. Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for training data and 89.95% for testing data.
Advancement in information and technology has made a major impact on medical science where the researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer is one such disease killing large... more
Advancement in information and technology has made a major impact on medical science where the researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer is one such disease killing large number of people around the world. Diagnosing the disease at its earliest instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases. Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for training data and 89.95% for testing data.
