Comparison with Sugeno Model and Measurement Of Cancer Risk Analysis By New Fuzzy Logic Approach
Comparison with Sugeno Model and Measurement Of Cancer Risk Analysis By New Fuzzy Logic Approach
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与Sugeno模型的比较以及通过新模糊逻辑方法进行癌症风险分析的测量
DOI：
10.5897/ajb11.2499
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发表时间：
2012
影响因子：
--
通讯作者：
K. Ayan
中科院分区：
文献类型：
--
作者：
A. Yılmaz;K. Ayan
由于医疗资源的限制和不能有效利用现有资源，每年有成千上万的人死于癌症。在医疗卫生系统中使用数字（定量）技术可以减少病人的损失。癌症是当今世界威胁人类生命的主要疾病。虽然每种类型的癌症的形成是不同的，但在研究和研究中确定，压力也会引发癌症类型。早期预防对于那些尚未患病的人来说非常重要，这些人死亡率高，治疗费用昂贵，如癌症。通过这种类型的研究，患病的可能性可能会降低，人们可以采取措施治疗疾病。在这项研究中，对于三种癌症类型选择作为试点，通过引入一种新的类型的模糊逻辑模型，揭示的风险，捕捉这些癌症类型的人和提供初步诊断的人，以消除这种风险的机会。在计算了风险结果之后，讨论并计算了压力对癌症的影响。由于这种类型的研究，人们将有机会采取措施，以防止患癌和患癌率可以降低。由于这项研究，强大的软件的演示文稿的目的，使相关技术用于健康领域和样本研究进行。此外，通过计算由新型模糊逻辑技术开发的模型的结果的性能测量值，揭示了新技术的性能状态，所述新型模糊逻辑技术用于在研究中选择作为试点的三种癌症类型和Takagi-Sugeno型模糊逻辑模型。关键词：模糊逻辑，人工智能，癌症，风险分析，初步诊断，软计算，新的模糊逻辑技术。
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 intelligence, cancer, risk analysis, preliminary diagnosis, soft computing, new fuzzy logic technique.