Diagnosis of breast cancer using case-based reasoning - Nurramlah, Abu Nasir (2012)
The objective for this thesis is to develop an intelligent decision support application for diagnosis of breast cancer using Case-Based Reasoning (CBR) algorithm for predict the class of cancer for patients. The number of expertises in the medical domain about the breast cancer is limited. Many patients have to wait too long to get their result from the check-up. The experience medical staffs are decreasing in number. When they retired, the new staffs will be replacing their places. So they have to learn many things related to their work. The application is very useful in the management of the problem and aids the inexperience physicians to check their diagnosis. It is to help the expert doctors or medical staffs in their breast cancer diagnosis. The methodology used in the application is Rapid Application Development (RAD) because it promotes the accuracy application development and delivery and reduced the cycle time. The application used the 100 data of Wisconsin Breast Cancer dataset for evaluating the CBR algorithm. This dataset is retrieved from UCI Machine Learning. The data used in this application consists of 9 attributes where the result of each case will be classified either non-cancerous (benign) or cancerous (malignant) group.