scholarly journals A Logistic Regression Model Based on the National Mammography Database Format to Aid Breast Cancer Diagnosis

2009 ◽  
Vol 192 (4) ◽  
pp. 1117-1127 ◽  
Author(s):  
Jagpreet Chhatwal ◽  
Oguzhan Alagoz ◽  
Mary J. Lindstrom ◽  
Charles E. Kahn ◽  
Katherine A. Shaffer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Cancer Diagnosis ◽  
Logistic Regression Model ◽  
Breast Cancer Diagnosis ◽  
Model Based

An Optimized Logistic Regression Model Based on the Maximum Entropy Estimation Under the Hesitant Fuzzy Environment

2021 ◽  
pp. 1-25
Author(s):  
Chenyang Song ◽  
Liguo Wang ◽  
Zeshui Xu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Maximum Entropy ◽  
Logistic Regression Model ◽  
Uncertain Information ◽  
Entropy Estimation ◽  
Model Based ◽  
Fuzzy Environment ◽  
Maximum Entropy Estimation ◽  
Specific Implementation

The logistic regression model is one of the most widely used classification models. In some practical situations, few samples and massive uncertain information bring more challenges to the application of the traditional logistic regression. This paper takes advantages of the hesitant fuzzy set (HFS) in depicting uncertain information and develops the logistic regression model under hesitant fuzzy environment. Considering the complexity and uncertainty in the application of this logistic regression, the concept of hesitant fuzzy information flow (HFIF) and the correlation coefficient between HFSs are introduced to determine the main factors. In order to better manage situations with small samples, a new optimized method based on the maximum entropy estimation is also proposed to determine the parameters. Then the Levenberg–Marquardt Algorithm (LMA) under hesitant fuzzy environment is developed to solve the parameter estimation problem with fewer samples and uncertain information in the logistic regression model. A specific implementation process for the optimized logistic regression model based on the maximum entropy estimation under the hesitant fuzzy environment is also provided. Moreover, we apply the proposed model to the prediction problem of Emergency Extreme Air Pollution Event (EEAPE). A comparative analysis and a sensitivity analysis are further conducted to illustrate the advantages of the optimized logistic regression model under hesitant fuzzy environment.

scholarly journals Empirical thresholding logistic regression model based on unbalanced cardiac patient data

2017 ◽  
Vol 121 ◽  
pp. 160-165 ◽  
Author(s):  
Iris Reychav ◽  
Lin Zhu ◽  
Roger McHaney ◽  
Yaron Arbel
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Patient Data ◽  
Cardiac Patient ◽  
Model Based
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