Abstract

Every year thousands of human mortality from cancer is due to limitation of medical sources and unable to use the existing sources effectively. Patient losses can be reduced by using the numerical (quantitative) techniques in the system of medical and health. Cancer is a genetic disease which is developed by the abnormal cell increase and cell growth as a result of deoxyribonucleic acid (DNA) damage and cells being out of the program. With this model, as earlier cancer is diagnosed, so the treatment would be that successful. In this study, risk of getting breast cancer of people is going to be deduced and the opportunity to destroy this risk will be suggested to the patient. Effects of stress to cancer are going to be examined after evaluating the risk value of cancer that is going to be evaluated on the basis of self resistance of the person to cancer expectance of risk result and aptitude to stress. In order to resolve the problem, the available figures have been evaluated; leading method and sample have been presented together with fuzzy logic model as a new modality. The reason for selection of fuzzy logic model in this study is that the system uses fuzzy logic model which provide effective results depending on uncertain verbal knowledge just like logic of human being. After receiving good results from the study; our system will make a pre-diagnosis for the people who possibly can have risk of getting cancer by the reason of working conditions or living standards. Therefore, this will enable these people to take precautions to the risk of cancer. Besides, the contribution of fuzzy logic model in the field of health and topics of artificial intelligence will also be examined in this study. Due to this type of study, people will have the chance to take measures against catching cancer and the rate of catching cancer can be decreased. Due to this study, the presentation of strong software is aimed, so that related techniques are used in the health field and sample studies are conducted.

Key words: Fuzzy logic, cancer, risk, analysis, breast cancer, stress, preliminary diagnosis, soft computing.