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Fuzzy logistic regression based on the least squares approach with application in clinical studies

机译:基于最小二乘法的模糊逻辑回归分析及其在临床研究中的应用

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To model fuzzy binary observations, a new model named "Fuzzy Logistic Regression" is proposed and discussed in this study. In fact, due to the vague nature of binary observations, no probability distribution can be considered for these data. Therefore, the ordinary logistic regression may not be appropriate. This study attempts to construct a fuzzy model based on possibility of success. These possibilities are defined by some linguistic terms such as..., low, medium, high.... Then, by use of the Extension principle, the logarithm transformation of "possibilistic odds" is modeled based on a set of crisp explanatory variables observations. Also, to estimate parameters in the proposed model, the least squares method in fuzzy linear regression is used. For evaluating the model, a criterion named the "capability index" is calculated. At the end, because of widespread applications of logistic regression in clinical studies and also, the abundance of vague observations in clinical diagnosis, the suspected cases to Systematic Lupus Erythematosus (SLE) disease is modeled based on some significant risk factors to detect the application of the model. The results showed that the proposed model could be a rational substituted model of an ordinary one in modeling the clinical vague status.
机译:为了对模糊二元观测值进行建模,提出并讨论了一个名为“模糊逻辑回归”的新模型。实际上,由于二元观测值的模糊性,无法为这些数据考虑概率分布。因此,普通的逻辑回归可能不合适。本研究试图基于成功的可能性构建模糊模型。这些可能性由一些语言术语定义,例如...,低,中,高...。然后,使用扩展原理,基于一组清晰的解释变量对“可能性赔率”的对数转换进行建模观察。另外,为了估计所提出模型中的参数,使用了模糊线性回归中的最小二乘法。为了评估模型,计算了一个称为“能力指数”的标准。最后,由于逻辑回归在临床研究中的广泛应用以及临床诊断中大量模糊的观察结果,因此基于一些重要的风险因素对可疑系统性红斑狼疮(SLE)疾病的病例进行了建模,以检测其应用。该模型。结果表明,所提出的模型在临床模糊状态建模中可以作为普通模型的合理替代模型。

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