A REVIEW ON DIFFERENT COMPUTING METHOD FOR BREAST CANCER DIAGNOSIS USING ARTIFICIAL NEURAL NETWORK AND DATAMINING TECHNIQUES.

Breast Cancer is one of the fatal diseases causing more number of deaths in women. . It is known as one of the most common cancers to afflict the female population. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial neural networks and data mining have featured in a wide range of medical fields .It has been widely used in intelligent breast cancer diagnosis. The aim of our study is to propose an approach for breast cancer distinguishing between different classes of breast cancer. This approach is based on the Wisconsin Diagnostic Breast Cancer and the classification of different types of breast cancer datasets. Breast cancer diagnosis has been approached by various machine learning and data mining techniques for many years. Artificial Neural Networks in most of the instances gives accurate results for the diagnosis of breast cancer and their use can also be extended to other diseases. Knowledge Discovery in Databases (KDD), which includes data mining classification techniques, is a popular research tool mostly for medical researchers to identify and exploit patterns and relationships among large number of variables, and made them able to predict the outcome of a disease using the historical cases stored within datasets. This paper presents a review on classification of Breast cancer using artificial neural network and various data mining classification techniques. Different techniques are used for the diagnosis and prognosis of breast cancer using artificial neural network data mining classification technique. The models were used namely multilayer perceptron (MLP) using back-propagation algorithm, probabilistic neural networks (PNN), learning vector quantization (LVQ) ,radial basis network (RBF), general regression neural network(GRNN), support vector machine (SVM), Adaptive Resonance Neural Networks (ARNN), ART, and Feed Forward Artificial Neural Networks and likewise in data mining .The performance of the network is evaluated using Wisconsin breast cancer data set for various training algorithm based on resulted accuracy. This survey can also help us to know about number of papers that are implemented to diagnose the breast cancer.