Using Data Mining Tools for Breast Cancer Prediction and Analysis

Breast Cancer is one of the most common disease that is responsible for high number of women's deaths every year. Despite the fact that cancer is treatable and healable in earliest stages, the huge number of patients are examined with cancer very late. Data mining process and classification are an efficient way to categorise the data particularly in medical fields, where those approaches are broadly used in diagnosis to make decision. This paper presents a performance comparison among the classifiers: Decision tree classifier (J4.8, Simple CART), Bayes classifier (NaiveBayes, Bayesian LogisticRegression). The Wisconsin Breast Cancer(original) dataset is used here and is taken from UCI Machine learning Repository. The main goal is to classify data of both the algorithms in terms of accuracy. Our experimental result shows that among all the classifiers, decision tree classifier i.e. Simple CART (98.13%) gives higher accuracy.