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Epidemiology of fat replacement of the right ventricular myocardium determined by multislice computed tomography using a logistic regression model

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INTERNATIONAL JOURNAL OF CARDIOLOGY 2007年第3期119卷 410-413页

作者： Imada, Megumi Funabashi, Nobusada Asano, Miki Uehara, Masae Hori, Yasuhiko Ueda, Marehiko Komuro, Issei Chiba Univ Grad Sch Med Dept Cardiovasc Sci & Med Chuo Ku Chiba 2608670 Japan

Purpose: We frequently observe fat replacement (FR) of the anterior wall of the right ventricular myocardium (RVM), but its epidemiological significance is not clear. Methods and materials: 49 consecutive subjects (28 males, 36-83 years old, median 67) underwent enhanced ECG-gated multislice CT (Light speed ultra 16, General Electrics, WI) and we retrospectively analyzed the presence of FR of RVM. A logistic model for predicting FR of RVM was constructed using age, sex, hypertension [HT], diabetes mellitus [DM], hyperlipidemia [HL] smoking, obesity (body mass index >25.0) and calcified and non-calcified plaques of coronary arteries (CA). Results: FR of RVM was detected in 21 subjects (12 males, 51-78 years old, median 67), 76% of whom had HT, 38% DM, 43% HL, 48% smoking history, 52% were obese, and 76% had calcified and 24% had non-calcified plaques of CA. Only obesity was significantly higher in FR (p<0.05). A logistic regression model showed, although there was a close association between obesity and an increased incidence of FR, it did not reach statistical significance (p=0.0515, relative risk 5.11). Conclusions: Obesity is significantly more common in cases of FR, and despite a negative multivariable analysis, may influence FR in the RVM. FR in obesity may occur independently of clinically-significant arrhythmia, which is different from ARVC. Thus, even with FR, obesity must be considered as a diagnosis before ARVC. (C) 2006 Elsevier Ireland Ltd. All rights reserved.

关键词： Fat replacement Right ventricular myocardium Multislice computed tomography logistic regression model

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On the stochastic restricted Liu estimator in logistic regression model

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2020年第15期90卷 2766-2788页

作者： Li, Yong Asar, Yasin Wu, Jibo Chongqing Univ Arts & Sci Key Lab Grp Graph Theories & Applicat Chongqing 402160 Peoples R China Necmettin Erbakan Univ Dept Math & Comp Sci Konya Turkey

In this paper, we study the effects of near-singularity which is known as multicollinearity in the binary logistic regression. Furthermore, we also assume the presence of stochastic non-sample linear restrictions. The well-known logistic Liu estimator is combined with the stochastic linear restrictions in order to propose a new method, namely, the stochastic restricted Liu estimation. Theoretical comparisons between the usual maximum likelihood estimator, Liu estimator, stochastic restricted maximum-likelihood estimator and the new stochastic restricted Liu estimator are derived using matrix mean-squared errors of the estimators. A Monte Carlo simulation experiment is designed to evaluate the performances of the listed estimators in terms of mean-squared error and mean absolute error criteria. Artificial data are used to show how to interpret the theorems. According to the results of the simulation, the new method beats the other estimators when the data matrix has the problem of collinearity along with the stochastic restrictions.

关键词： Multicollinearity stochastic restricted Liu estimator Liu logistic estimator logistic regression model

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Developing a logistic regression model to predict spontaneous preterm birth from maternal socio-demographic and obstetric history at initial pregnancy registration

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BMC PREGNANCY AND CHILDBIRTH 2024年第1期24卷 1页

作者： Narice, Brenda F. Labib, Mariam Wang, Mengxiao Byrne, Victoria Shepherd, Joanna Lang, Z. Q. Anumba, Dilly O. C. Univ Sheffield Sch Med & Populat Hlth Sheffield England Univ Sheffield Dept Automat Control & Syst Engn Sheffield England

Background Current predictive machine learning techniques for spontaneous preterm birth heavily rely on a history of previous preterm birth and/or costly techniques such as fetal fibronectin and ultrasound measurement of cervical length to the disadvantage of those considered at low risk and/or those who have no access to more expensive screening tools. Aims and objectives We aimed to develop a predictive model for spontaneous preterm delivery < 37 weeks using socio-demographic and clinical data readily available at booking -an approach which could be suitable for all women regardless of their previous obstetric history. Methods We developed a logistic regression model using seven feature variables derived from maternal socio-demographic and obstetric history from a preterm birth (n = 917) and a matched full-term (n = 100) cohort in 2018 and 2020 at a tertiary obstetric unit in the UK. A three-fold cross-validation technique was applied with subsets for data training and testing in Python (R) (version 3.8) using the most predictive factors. The model performance was then compared to the previously published predictive algorithms. Results The retrospective model showed good predictive accuracy with an AUC of 0.76 (95% CI: 0.71-0.83) for spontaneous preterm birth, with a sensitivity and specificity of 0.71 (95% CI: 0.66-0.76) and 0.78 (95% CI: 0.63-0.88) respectively based on seven variables: maternal age, BMI, ethnicity, smoking, gestational type, substance misuse and parity/obstetric history. Conclusion Pending further validation, our observations suggest that key maternal demographic features, incorporated into a traditional mathematical model, have promising predictive utility for spontaneous preterm birth in pregnant women in our region without the need for cervical length and/or fetal fibronectin.

关键词： Preterm birth Pregnancy Prediction logistic regression model Machine learning

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Incorporating spectral data into logistic regression model to classify land cover: a case study in Mt. Qomolangma (Everest) National Nature Preserve

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2012年第10期26卷 1845-1862页

作者： Gao, Jungang Zhang, Yili Chinese Acad Sci Inst Geog Sci & Nat Resources Res Dept Land Use Land Cover Change & Land Resources Beijing Peoples R China Chinese Acad Sci Grad Sch Dept Resources & Environm Beijing Peoples R China

Only few models for land-cover classification incorporated spectral data into ordinary logistic regression (OL model) in the Mt. Qomolangma (Everest) National Nature Preserve (QNNP) in China. In this study, spectral variables were incorporated into OL model and autologistic regression (AL) model to classify six main land covers. Twelve environmental variables and seven spectral variables of 10,000 stratified random sites in the QNNP were quantified and analyzed;OL model, AL model, OL model with spectral data (OLM model), and AL model with spectral data (ALM model) were estimated. The OLM and ALM models produced better estimates of regression coefficients and significantly improved model performance and overall accuracy for the grassland, sparsely vegetated land, and bare land compared with OL and AL models.

关键词： logistic regression model autologistic regression model spectral variable land cover classification Mt. Qomolangma National Nature Preserve

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Study on Management Characteristics and Earnings Forecast Disclosure Willingness Based on logistic regression model

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JOURNAL OF COMPUTERS 2012年第8期7卷 1951-1958页

作者： Nan, Xu Yanshan Univ Sch Econ & Management Qinhuangdao Peoples R China

This paper studied the relationship between the management characteristics and the earnings forecast disclosure willingness within Chinese listed companies. Management characteristics were being defined and summarized as four aspects: structure characteristics, incentive characteristics, background characteristics and external governance mechanism. The logistic regression model was applied to carry out an empirical analysis on the effect of specific variables of management characteristics on the earnings forecast disclosure willingness. The results show that the board size, managers' changes, asset-liability ratio and managers' stockholdings are the major factors. The board size relates with the earnings forecast disclosure willingness significant negative;manager's changes, assetliability ratio and managers' stockholdings relate with the earnings forecast disclosure willingness significant positive. Other variables of management characteristics have no significant influence.

关键词： management characteristics earnings forecast disclosure willingness logistic regression model

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Efficacy of logistic regression model based on multiparametric ultrasound in assessment of cervical lymphadenopathy - a retrospective study

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DENTOMAXILLOFACIAL RADIOLOGY 2022年第2期51卷 1-9页

作者： Cai, Dongyan Wu, Size Hainan Med Univ Dept Ultrasound Affiliated Hosp 1 Haikou Hainan Peoples R China Sun Yat Sen Univ Affiliated Hosp 8 Dept Med Ultrasound Shenzhen Peoples R China

Objectives: To investigate whether a multiparametric ultrasound (MPUS) diagnostic model improves differential diagnosis of benign and malignant cervical lymph nodes. Methods: MPUS evaluation was performed on 87 lesions in 86 patients, and related characteristics and parameters of the patients and lesions were studied and logistic regression models based on the MPUS characteristics of cervical lymph nodes were built. A receiver operating characteristic curve and area under the curve (AUC) were built for the evaluation of diagnostic performances. Results: Of the 87 lesions in 86 patients, there were 31 benign and 56 malignant lesions. regression models for Duplex ultrasound and MPUS were established. The Duplex ultrasound regression model showed a sensitivity, specificity, positive predictive value and negative predictive value of 94.4, 61.3, 86.3 and 80.9%, respectively. The predictive accuracy was 82.4%, and the AUC was 0.861. The MPUS regression model showed a sensitivity, specificity, positive predictive value and negative predictive value of 98.1, 61.3, 81.5 and 95.0%, respectively. The predictive accuracy was 84.7%, and the AUC was 0.894. The differences in AUCs between the Duplex ultrasound model and MPUS model, ultrasound model and ultrasonic elastography (UE), and Duplex ultrasound and UE were not significant (all p > 0.05);the differences in AUCs between the MPUS model and Duplex ultrasound, Duplex ultrasound model and Duplex ultrasound, and MPUS model and UE were significant (all p < 0.05). Conclusions: The Duplex ultrasound and MPUS models achieve significantly higher diagnostic performance for differentiating between benign and malignant cervical lymph nodes.

关键词： Cervical lymph node Ultrasonography Ultrasound elastography logistic regression model

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Evaluation of a logistic regression model for predicting liver necroinflammation in hepatitis B e antigen-negative chronic hepatitis B patients with normal and minimally increased alanine aminotransferase levels

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JOURNAL OF VIRAL HEPATITIS 2019年第Sup1期26卷 42-49页

作者： Xie, Yao Yi, Wei Zhang, Lu Lu, Yao Hao, Hong-Xiao Gao, Yuan-Jiao Ran, Chong-Ping Lu, Hui-Hui Chen, Qi-Qi Shen, Ge Wu, Shu-Ling Chang, Ming Ping-Hu, Lei Liu, Rui-Yu Sun, Lei Wan, Gang Li, Ming-Hui Capital Med Univ Beijing Ditan Hosp Dept Hepatol Div 2 Jing Shun Dong Jie 8 Beijing 100015 Peoples R China Capital Med Univ Beijing Ditan Hosp Dept Gynecol & Obstet Beijing Peoples R China Capital Med Univ Beijing Ditan Hosp Dept Pathol Beijing Peoples R China Capital Med Univ Beijing Ditan Hosp Med Records & Stat Dept Beijing Peoples R China

Liver necroinflammation is the indicator for treating patients with chronic hepatitis B (CHB) infection. However, there is no suitable non-invasive index for diagnosing liver necroinflammation. This study aimed to create a non-invasive index to predict liver necroinflammation in patients who lack clear-cut clinical inflammation parameters. Patients who were hepatitis B e antigen (HBeAg)-negative and underwent liver histological diagnosis, had a normal or minimally increased alanine aminotransferase (ALT) level were enrolled. Liver necroinflammation was defined as histological active index >= 4. A logistic regression model (LRM) was established based on the parameters independently associated with liver necroinflammation. Of all 550 patients, 36.73% had necroinflammation. In patients with an abnormal ALT level, the rate of necroinflammation was 52.49%. The area under the curve (AUC) of the ALT level for predicting necroinflammation was 0.655 (95% confidence interval [CI], 0.609-0.702), and that of the HBV DNA level >= 2000 IU/mL combined with an abnormal ALT level was 0.618. By using the LRM, the AUC improved to 0.769 (95% CI, 0.723-0.815) with a Youden index of 0.519 and diagnostic accuracy of 75.3%. The cutoff value >= 0.7 in the LRM had a specificity of 97.4% and positive predictive value of 85.0% for predicting necroinflammation. By using the cutoff value <0.15 in the LRM, the presence of necroinflammation could be excluded with a negative predictive value of 90.8%. This study indicated that the LRM can be used to effectively diagnose liver necroinflammation in HBeAg-negative patients with CHB who have normal or minimally elevated ALT levels.

关键词： ALT level chronic hepatitis B HBeAg negative liver inflammation logistic regression model

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Preliminary phi-divergence test estimators for linear restrictions in a logistic regression model

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STATISTICAL PAPERS 2009年第2期50卷 277-300页

作者： Menendez, M. L. Pardo, L. Pardo, M. C. Tech Univ Dept Appl Math ETSAM Madrid Spain Univ Complutense Madrid Dept Stat & ORI Madrid Spain

The problem of estimation of the parameters in a logistic regression model is considered under multicollinearity situation when it is suspected that the parameter of the logistic regression model may be restricted to a subspace. We study the properties of the preliminary test based on the minimum phi-divergence estimator as well as in the phi-divergence test statistic. The minimum phi-divergence estimator is a natural extension of the maximum likelihood estimator and the phi-divergence test statistics is a family of the test statistics for testing the hypothesis that the regression coefficients may be restricted to a subspace.

关键词： logistic regression model Phi-divergence test statistics Minimum phi-divergence estimator General linear hypotheses Preliminary phi-divergence test estimator

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Alternating Direction Method of Multipliers for l_(1)-l_(2)-Regularized logistic regression model

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Journal of the Operations Research Society of China 2016年第2期4卷 243-253页

作者： Yan-Qin Bai Kai-Ji Shen Department of Mathematics Shanghai UniversityShanghai 200444China

logistic regression has been proved as a promising method for machine learning,which focuses on the problem of *** this paper,we present anl_(1)-l_(2)-regularized logistic regression model,where thel1-norm is responsible for yielding a sparse logistic regression classifier and thel_(2)-norm for keeping betlter classification *** solve thel_(1)-l_(2)-regularized logistic regression model,we develop an alternating direction method of multipliers with embedding limitedlBroyden-Fletcher-Goldfarb-Shanno(L-BFGS)***,we implement our model for binary classification problems by using real data examples selected from the University of California,Irvine Machines Learning Repository(UCI Repository).We compare our numerical results with those obtained by the well-known LIBSVM and SVM-Light *** numerical results show that ourl_(1)-l_(2)-regularized logisltic regression model achieves better classification and less CPU Time.

关键词： Classification problems logistic regression model Sparsity Alternating direction method of multipliers

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Semiparametric estimation of logistic regression model with missing covariates and outcome

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METRIKA 2012年第5期75卷 621-653页

作者： Lee, Shen-Ming Li, Chin-Shang Hsieh, Shu-Hui Huang, Li-Hui Univ Calif Davis Div Biostat Dept Publ Hlth Sci Davis CA 95616 USA Feng Chia Univ Dept Stat Taichung 40724 Taiwan Overseas Chinese Univ Dept Int Trade Taichung Taiwan

We consider a semiparametric method to estimate logistic regression models with missing both covariates and an outcome variable, and propose two new estimators. The first, which is based solely on the validation set, is an extension of the validation likelihood estimator of Breslow and Cain (Biometrika 75:11-20, 1988). The second is a joint conditional likelihood estimator based on the validation and non-validation data sets. Both estimators are semiparametric as they do not require any model assumptions regarding the missing data mechanism nor the specification of the conditional distribution of the missing covariates given the observed covariates. The asymptotic distribution theory is developed under the assumption that all covariate variables are categorical. The finite-sample properties of the proposed estimators are investigated through simulation studies showing that the joint conditional likelihood estimator is the most efficient. A cable TV survey data set from Taiwan is used to illustrate the practical use of the proposed methodology.

关键词： Missing value logistic regression model Missing outcome Missing covariates

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