scholarly journals Application of Business Risk Prediction Model: Based on the Logistic Regression Model

2014 ◽  
Vol 9 (7) ◽  
Author(s):  
Tang Tang ◽  
Shen Le-ping
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Risk Prediction ◽  
Logistic Regression Model ◽  
Risk Prediction Model ◽  
Business Risk ◽  
Model Based

scholarly journals Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Jessica K. Sexton ◽  
Michael Coory ◽  
Sailesh Kumar ◽  
Gordon Smith ◽  
Adrienne Gordon ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Risk Prediction ◽  
Late Pregnancy ◽  
Risk Prediction Model ◽  
Past Century ◽  
Reporting Standards ◽  
Robust Method ◽  
Predictive Values ◽  
Multivariable Logistic Regression

Abstract Background Despite advances in the care of women and their babies in the past century, an estimated 1.7 million babies are born still each year throughout the world. A robust method to estimate a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform decision-making around the timing of birth to reduce the risk of stillbirth from 35 weeks of gestation in Australia, a high-resource setting. Methods This is a protocol for a cross-sectional study of all late-pregnancy births in Australia (2005–2015) from 35 weeks of gestation including 5188 stillbirths among 3.1 million births at an estimated rate of 1.7 stillbirths per 1000 births. A multivariable logistic regression model will be developed in line with current TransparentReporting of a multivariable prediction model forIndividualPrognosis orDiagnosis (TRIPOD) guidelines to estimate the gestation-specific probability of stillbirth with prediction intervals. Candidate predictors were identified from systematic reviews and clinical consultation and will be described through univariable regression analysis. To generate a final model, elimination by backward stepwise multivariable logistic regression will be performed. The model will be internally validated using bootstrapping with 1000 repetitions and externally validated using a temporally unique dataset. Overall model performance will be assessed with R2, calibration, and discrimination. Calibration will be reported using a calibration plot with 95% confidence intervals (α = 0.05). Discrimination will be measured by the C-statistic and area underneath the receiver-operator curves. Clinical usefulness will be reported as positive and negative predictive values, and a decision curve analysis will be considered. Discussion A robust method to predict a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform timely, appropriate care to reduce stillbirth. Among existing prediction models designed for obstetric use, few have been subject to internal and external validation and many fail to meet recommended reporting standards. In developing a risk prediction model for late-gestation stillbirth with both providers and pregnant women in mind, we endeavor to develop a validated model for clinical use in Australia that meets current reporting standards.

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 Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiao-Ying Xie ◽  
Ming-Wei Wang ◽  
Zu-Ying Hu ◽  
Cheng-Jian Cao ◽  
Cong Wang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Lasso Regression

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.

Prediction of ventricular arrhythmias in patients with a spontaneous Brugada type 1 pattern: the key is in the electrocardiogram

EP Europace ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 1400-1409 ◽  
Author(s):  
Antoine Delinière ◽  
Adrian Baranchuk ◽  
Joris Giai ◽  
Francis Bessiere ◽  
Delphine Maucort-Boulch ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Univariate Analysis ◽  
Sudden Cardiac Arrest ◽  
International Study ◽  
Risk Prediction Model ◽  
Adult Patients ◽  
Penalized Spline ◽  
Model Based

Abstract Aims There is currently no reliable tool to quantify the risks of ventricular fibrillation or sudden cardiac arrest (VF/SCA) in patients with spontaneous Brugada type 1 pattern (BrT1). Previous studies showed that electrocardiographic (ECG) markers of depolarization or repolarization disorders might indicate elevated risk. We aimed to design a VF/SCA risk prediction model based on ECG analyses for adult patients with spontaneous BrT1. Methods and results This retrospective multicentre international study analysed ECG data from 115 patients (mean age 45.1 ± 12.8 years, 105 males) with spontaneous BrT1. Of these, 45 patients had experienced VF/SCA and 70 patients did not experience VF/SCA. Among 10 ECG markers, a univariate analysis showed significant associations between VF/SCA and maximum corrected Tpeak–Tend intervals ≥100 ms in precordial leads (LMaxTpec) (P < 0.001), BrT1 in a peripheral lead (pT1) (P = 0.004), early repolarization in inferolateral leads (ER) (P < 0.001), and QRS duration ≥120 ms in lead V2 (P = 0.002). The Cox multivariate analysis revealed four predictors of VF/SCA: the LMaxTpec [hazard ratio (HR) 8.3, 95% confidence interval (CI) 2.4–28.5; P < 0.001], LMaxTpec + ER (HR 14.9, 95% CI 4.2–53.1; P < 0.001), LMaxTpec + pT1 (HR 17.2, 95% CI 4.1–72; P < 0.001), and LMaxTpec + pT1 + ER (HR 23.5, 95% CI 6–93; P < 0.001). Our multidimensional penalized spline model predicted the 1-year risk of VF/SCA, based on age and these markers. Conclusion LMaxTpec and its association with pT1 and/or ER indicated elevated VF/SCA risk in adult patients with spontaneous BrT1. We successfully developed a simple risk prediction model based on age and these ECG markers.

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