A CAD Tool for Breast Cancer Prediction using Naive Bayes Classifier

摘要

Even though today's medical field is technologically advanced, some diseases still haunt the human race by posing a hustle in its existence. In addition to the sophisticated tools and techniques for disease diagnosis, recent use of information and communication technology (ICT) has also tightened its spine to serve this noble cause. To have an interior view of the body without surgery, medical imaging is a predominant technique behind early/automatic diagnosing and detecting diseases. Image mammography is the primary asset assisting doctors for having a projection of the interiors of breast tissues, thus offering a crucial big stick in the diagnosis of malignancyon- malignancy in the tissues. Using certain supervised and unsupervised filters offered by Weka on top of BCDR-F01 cancer benhcmark dataset, this work intends to increase the objectivity of clinical diagnostics. Misclassification cost of Naive Bayes algorithm is measured and compared the same with misclassification cost measured after applying respective filters. The results show the accuracies in case of supervised attribute DISCRETIZE filter, supervised instance RESAMPLE filter and unsupervised attribute PKIIDiscretize filter get amplified from 73.7569 % to 81.768 %, 85.0829 %, and 78.7293 % and only in case of unsupervised instance RESAMPLE filter, it shows a minute decrease to 73.4807 %.