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AI InsightFurthermore, the performance status of the new technique is revealed by calculating performance measurements by the outcomes of the models developed by the new type of fuzzy logic technique for 3 cancer types selected as a pilot in the Mamdani type of fuzzy logic model.
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Özet
Cancer is the leading life-threatening disease for people in today's world. Although cancer formation is different for each type of cancer, it has been determined by studies and research that stress also triggers cancer types. Early precaution is very important for people who have not fallen ill yet with a disease like cancer that has a high mortality rate and expensive treatment. With this study, we expound that the possibility of developing such disease may be decreased and people could take measures against it. For the 3 cancer types selected as pilot work by introducing a fuzzy logic model, the risks for acquiring these cancer types and preliminary diagnosis for the person to remove these risks are presented. After calculating the risk outcome, the effect of stress on cancer is discussed and determined. Within the study, a fuzzy logic technique that can easily be adapted to other industry studies, as well, is applied to the health industry and effective software for application is developed. Due to this type of study, people will have the chance to take measures against developing cancer and the rate of suffering from cancer may be decreased. Furthermore, the performance status of the new technique is revealed by calculating performance measurements by the outcomes of the models developed by the new type of fuzzy logic technique for 3 cancer types selected as a pilot in the Mamdani type of fuzzy logic model.
Abstract
Cancer is the leading life-threatening disease for people in today's world. Although cancer formation is different for each type of cancer, it has been determined by studies and research that stress also triggers cancer types. Early precaution is very important for people who have not fallen ill yet with a disease like cancer that has a high mortality rate and expensive treatment. With this study, we expound that the possibility of developing such disease may be decreased and people could take measures against it. For the 3 cancer types selected as pilot work by introducing a fuzzy logic model, the risks for acquiring these cancer types and preliminary diagnosis for the person to remove these risks are presented. After calculating the risk outcome, the effect of stress on cancer is discussed and determined. Within the study, a fuzzy logic technique that can easily be adapted to other industry studies, as well, is applied to the health industry and effective software for application is developed. Due to this type of study, people will have the chance to take measures against developing cancer and the rate of suffering from cancer may be decreased. Furthermore, the performance status of the new technique is revealed by calculating performance measurements by the outcomes of the models developed by the new type of fuzzy logic technique for 3 cancer types selected as a pilot in the Mamdani type of fuzzy logic model.