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Endogeneity in Logistic Regression Models
Emerging Infectious Diseases, 2005 To the Editor: Ethelberg et al. (1) report on a study of the determinants of hemolytic uremic syndrome resulting from Shiga toxin–producing Escherichia coli. The dataset is relatively small, and the authors use stepwise logistic regression models to detect small differences.
George Avery +2 more
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Quantitative Models of Fungi Interaction--based on Logistic models [PDF]
E3S Web of Conferences, 2021 As the key medium for decomposing wood fibers, fungi play a vital role in promoting the carbon cycle. The purpose of this paper is to establish mathematic models describing the process of fungi decomposing litter and wood fiber. The paper comprehensively
Zhang Yunfei
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Development and Comparison of Three Data Models for Predicting Diabetes Mellitus Using Risk Factors in a Nigerian Population [PDF]
Healthcare Informatics Research, 2022 Objectives This study developed and compared the performance of three widely used predictive models—logistic regression (LR), artificial neural network (ANN), and decision tree (DT)—to predict diabetes mellitus using the socio-demographic, lifestyle, and
Oluwakemi Odukoya +7 more
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Birth Patterns and Delayed Breastfeeding Initiation in Indonesia [PDF]
Journal of Preventive Medicine and Public Health, 2020 Objectives This study was conducted to examine the association between birth patterns (defined in terms of birth order and interval) with delayed breastfeeding initiation in Indonesia.
Tika Dwi Tama +3 more
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Variable Selection for Spatial Logistic Autoregressive Models
Mathematics, 2022 When the spatial response variables are discrete, the spatial logistic autoregressive model adds an additional network structure to the ordinary logistic regression model to improve the classification accuracy. With the emergence of high-dimensional data
Jiaxuan Liang +4 more
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Machine Learning, 2003 Tree induction methods and linear models are popular techniques for supervised learning tasks, both for the prediction of nominal classes and continuous numeric values. For predicting numeric quantities, there has been work on combining these two schemes into 'model trees', i.e. trees that contain linear regression functions at the leaves.
Landwehr, Niels, Hall, Mark, Frank, Eibe
openaire +5 more sources
Group Logistic Regression Models with lp,q Regularization
Mathematics, 2022 In this paper, we proposed a logistic regression model with lp,q regularization that could give a group sparse solution. The model could be applied to variable-selection problems with sparse group structures. In the context of big data, the solutions for
Yanfang Zhang, Chuanhua Wei, Xiaolin Liu
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Study on Risk Factors of Peripheral Neuropathy in Type 2 Diabetes Mellitus and Establishment of Prediction Model [PDF]
Diabetes & Metabolism Journal, 2021 Background Diabetic peripheral neuropathy (DPN) is one of the most serious complications of type 2 diabetes mellitus (T2DM). DPN increases the risk of ulcers, foot infections, and noninvasive amputations, ultimately leading to long-term disability ...
Birong Wu, Zheyun Niu, Fan Hu
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Chinese Journal of Contemporary Neurology and Neurosurgery, 2022 Objective To analyze the imaging data of patients with hyperacute hypertensive cerebral hemorrhage with leukoaraiosis (LA), and to evaluate the possibility of unstable CT signs of hematoma as a predictor of hematoma enlargement risk.
WANG Wei +4 more
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Chinese Journal of Contemporary Neurology and Neurosurgery, 2022 Objective To explore the influencing factors of long⁃term prognosis of patients with spontaneous cerebellar hemorrhage (SCH) after surgery. Methods Total 121 patients with SCH admitted to the Second Affiliated Hospital of Air Force Military Medical ...
ZHAO Shun⁃zhong +6 more
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