Comparison with Sugeno Model and Measurement Of Cancer Risk Analysis By New Fuzzy Logic Approach

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 the leading life-threatening disease for people in today’s world. Although cancer formation is different for each type of cancer, it is determined in studies and research conducted that stress also triggers cancer types. Early precaution is very important for the people who have not been sick yet that have high mortality rate and expensive treatment such as cancer. With this type of study, the possibility of getting disease may decrease and people can take measures for the disease. In this study, for the three cancer types selected as pilot by introducing a new type of fuzzy logic model, the opportunity of revealing of risks for catching these cancer types of people and the opportunity of providing preliminary diagnosis to the person to remove this risk are presented. After the calculation of risk outcome, the effect of stress on cancer is discussed and calculated. 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. Furthermore, the performance status of the new technique was revealed by calculating performance measurements of the outcomes of the models developed by the new type of fuzzy logic technique for three cancer types selected as pilot within the study and Takagi-Sugeno type of fuzzy logic model.

Key words: Fuzzy logic, artificial ıntelligence, cancer, risk analysis, preliminary diagnosis, soft computing, new fuzzy logic technique.