{"title":"Fuzzy Logic Alternative for Analysis in the Biomedical Sciences","authors":"I. Virant-Klun , J. Virant","doi":"10.1006/cbmr.1999.1517","DOIUrl":null,"url":null,"abstract":"<div><p>Fuzzy logic brings new possibilities into control, modeling, data analysis, diagnostics, decision making, and other working fields in biomedical sciences. This paper presents how fuzzy logic can be used as an alternative or supplement to statistics in biomedical analysis. It shows an adaptive neuro-fuzzy inference computing in comparison with linear and curvilinear regression. The main goal of this presentation is to involve fuzzy logic in biomedical research. Thus, we carried out a mathematical treatment of the clinical sample, semen of infertile man, with the independent variable Concentration of spermatozoa and the dependent variable Number of spermatozoa by 230 observations.</p></div>","PeriodicalId":75733,"journal":{"name":"Computers and biomedical research, an international journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1999-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cbmr.1999.1517","citationCount":"73","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and biomedical research, an international journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010480999915173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 73
Abstract
Fuzzy logic brings new possibilities into control, modeling, data analysis, diagnostics, decision making, and other working fields in biomedical sciences. This paper presents how fuzzy logic can be used as an alternative or supplement to statistics in biomedical analysis. It shows an adaptive neuro-fuzzy inference computing in comparison with linear and curvilinear regression. The main goal of this presentation is to involve fuzzy logic in biomedical research. Thus, we carried out a mathematical treatment of the clinical sample, semen of infertile man, with the independent variable Concentration of spermatozoa and the dependent variable Number of spermatozoa by 230 observations.
模糊逻辑为生物医学中的控制、建模、数据分析、诊断、决策和其他工作领域带来了新的可能性。本文介绍了模糊逻辑如何在生物医学分析中作为统计学的替代或补充。与线性回归和曲线回归相比,它显示出一种自适应神经模糊推理计算。本演讲的主要目的是将模糊逻辑应用于生物医学研究。因此,我们通过230次观察,以精子浓度(Concentration of sperm)为自变量,以精子数量(Number of sperm)为因变量,对临床样本不孕症患者精液进行了数学处理。