Bayes gaussian classification of wisconsin breast cancer database

The correct pattern classification of breast cancer is an important medical problem. Breast cancer etiologies remain unclear and no single dominant cause has emerged. Prevention is still a mystery and the best way to improve patient survival is through early detection. If the cancerous cells are detected before they spread to other organs, the survival rate is greater than 97 percent.A major class of problems in medical science involves disease diagnosis based on various tests performed on patients. For this reason, the use of classifier systems in medical diagnosis is gradually increasing. There is no doubt that data evaluation taken from patients and experts decisions are the most important factors in diagnosis. Besides, artificial intelligence classification techniques can enhance current research. Classification systems, through minimizing possible errors likely produced due to tiredness or lack of experience, can provide more detailed medical data that can be checked in a shorter period.In this study, we focused on developing a medical decision-making application using Bayes Gaussian classification method. At the first step, theoretical derivations are adopted into our problem then we used MATLAB to write a computer program to be able to test developed algorithm.The purposed medical decision making system has been applied on the task of diagnosing breast cancer. Test of the developed classifier is carried out by using Wisconsin Breast Cancer Database. The 10-fold cross validation results show that overall accuracy is 94.38 percent.