An empirical study of supervised learning methods for breast cancer diseases

Touch-and-go issues related to medic-able of breast cancer diseases is a matter of disquiet globally pertaining to current research scenario, nevertheless image processing techniques like Artificial Neural Network (ANN) and decision Tree algorithm have been used extensively for identifying breast cancer in patients. Realizing the aforementioned after math, the current research delivers the results of an empirical study and comparative analysis of supervised learning strategies which utilizes four different types of datasets that culminate in the proposition of a classifier, called MMDBM (Mixed Mode Database Miner) as one of the best classifiers among 19 supervised learning techniques. The proposed classifier has affirms a higher rate of accuracy in detecting breast cancer cases from the given datasets.