Diagnostic methods and risk analysis based on fuzzy soft information
作者机构：Punjab University College of Information echnology University of the Punjab Old Campus Lahore Pakistan University of Management and Technology Lahore Pakistan Department of Mathematics University of the Punjab New Campus Lahore Pakistan
出 版 物：《International Journal of Biomathematics》 (生物数学学报（英文版）)
年 卷 期：2018年第11卷第8期
页 面：1-34页
核心收录：
学科分类：0701[理学-数学] 070101[理学-基础数学]
主 题：Fuzzy soft set similarity measure fuzzy soft metrics algorithm time complexity chikungunya malaria dengue mouth cancer serum total malondialdehyde serumproton donor capacity
摘 要：In our daily life problem we face uncertainties in making right decisions. In this study, we propose two different decision-making problems in medical field. The first problem is fever diagnosing and second problem is mouth cancer risk analysis. In the first problem, we use fuzzy soft similarity measures and fuzzy soft matrix operations to diagnose the type of fever. We consider a hypothetical case study and manipulate similarity measures on it. Our work diagnoses different patients having similar symptoms. We also develop a small application using JAVA. In the second problem, we perform risk analysis of mouth cancer. The proposed fuzzy soft expert system takes two biochemical parameters as inputs that is, serum total malondialdehyde (MDA), and serum proton donors capacity (donors_protons) and determines the risk of mouth cancer. Our study facilitates doctors by diagnosing mouth cancer at its earlier stages. There are four main components of our fuzzy soft expert system. The first component is named as fuzzification which converts crisp input into linguistic variables and formulates fuzzy sets. The second component transforms fuzzy sets into their respective fuzzy soft sets. The third coinponent determines indispensable parameters and perforins parameter reduction. The fourth component performs risk analysis by using algorithm. We use exemplary dataset and run all the components of fuzzy soft expert system to compute cancer risk.
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