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.

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.

No abstract

No abstract