Data mining is the process of analyzing large quantities of data and summarizing it into useful information. In medical diagnoses the role of data mining approaches increasing rapidly. Particularly Classification algorithms are very helpful in classifying the data, which is important in decision making process for medical practitioners. Further to enhance the classifier accuracy various pre-processing techniques and ensemble techniques were developed. In this study a hybrid approach, CART classifier with feature selection and bagging technique has been considered to evaluate the performance in terms of accuracy and time for classification of various breast cancer datasets.

In data mining, classification is one of the significant techniques with applications in fraud detection, Artificial intelligence, Medical Diagnosis and many other fields. Classification of objects based on their features into predefined categories is a widely studied problem. Decision trees are very much useful to diagnose a patient problem by the physicians. Decision tree classifiers are used extensively for diagnosis of breast tumour in ultrasonic images, ovarian cancer and heart sound diagnosis. In this paper, performance of decision tree induction classifiers on various medical data sets in terms of accuracy and time complexity are analysed.

Classification is perhaps the most familiar and popular data mining technique. Inspired by biological neural networks, Artificial Neural Networks are developed to mimic the characteristics such as robustness and fault tolerance. To perform classification task of medical data, the neural network is trained. To speed up the training process parallel approach is adopted. In this paper a parallel approach by using neural network technique is proposed to help in the diagnosis of breast cancer. The neural network is trained with breast cancer data base by using feed forward neural network model and backpropagation learning algorithm with momentum and variable learning rate. The performance of the network is evaluated. The experimental result shows that by applying parallel approach in neural network model yields efficient result.

ABSTRACT

Data Mining is a technique to extract the hidden knowledge of information. Among several data mining methods classification is especially useful in the field of medical diagnosis for decision making. In this study, a hybrid approach: CART decision tree classifier with feature selection and boosting ensemble method has been considered to evaluate the performance of classifier. Various Breast cancer data sets are considered for this study as breast cancer is one of the leading causes of death in women.

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