scholarly journals Study and Application on Risk Assessment Method of Coal Worker Pneumoconiosis Based on Logistic Regression Model

2018 ◽  
Vol 8 (3) ◽  
pp. 157
Author(s):  
Qian Zhang ◽  
Deyin Huang ◽  
Minyan Li
Keyword(s):  
Risk Assessment ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Assessment Method ◽  
Risk Assessment Method

Geohazard Risk Assessment Method Based on Logistic Regression Model

2012 ◽  
Vol 588-589 ◽  
pp. 1934-1937
Author(s):  
Si Cheng ◽  
Yun Sheng Wang ◽  
Fei Yu Chen
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Assessment Method ◽  
Risk Assessment Method ◽  
Geological Factor ◽  
Independent Variable ◽  
Geohazard Risk ◽  
Relationship Of ◽  
The Relationship

Logistic regression model refers a regress analysis contains two types of variants. In geohazard analysis, each geological factor can be defined as independent variable, whether a geohazard happened or not can be defined as a dependent variable. 1 represents an occurrence of a hazard while 0 represents a hazard doesn’t break out. Because those factors aren’t continual variable, lineal regress is inadequate to deduce the relationship of such kind of independent and dependent variable. Therefore using logistic regress method is a feasible way to solve such technique problem.

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.

scholarly journals Establishment of Metabolic Syndrome Prediction Model for Occupational Population based on the Lasso Regression Algorithm

2020 ◽  
Author(s):  
Qiao-Ying Xie ◽  
Ming-Wei Wang ◽  
Zu-Ying Hu ◽  
Yan-Ming Chu ◽  
Cheng-Jian Cao ◽  
...  
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

Abstract Background: Metabolic syndrome (MS) screening is important for the early detection of 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. Finally, the screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. Results: A total of 2844 occupational workers were included, and 10 biomarkers related to MS were screened. The area under the curve (AUC) value for non-Lasso and Lasso regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk factors were basophil absolute count (OR: 3.38), platelet packed volume (OR: 2.63), leukocyte count (OR: 2.01), red blood cell count (OR: 1.99), and alanine aminotransferase level (OR: 1.53). Conclusion: The risk assessment model based on the Lasso regression algorithm helped identify Metabolic syndrome with high accuracy in physically examining an occupational population.

scholarly journals Credit risk assessment of agricultural enterprises in the Republic of Serbia: Logistic regression vs discriminant analysis

2021 ◽  
Vol 68 (4) ◽  
pp. 881-894
Author(s):  
Dragana Tekić ◽  
Beba Mutavdžić ◽  
Dragan Milić ◽  
Nebojša Novković ◽  
Vladislav Zekić ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Logistic Regression ◽  
Discriminant Analysis ◽  
Credit Risk ◽  
Regression Model ◽  
Financial Stability ◽  
Logistic Regression Model ◽  
Agricultural Enterprises ◽  
Credit Risk Assessment ◽  
The Republic

Credit risk assessment of agricultural enterprises in the Republic of Serbia was analyzed in this research by applying discriminant analysis and logistic regressions. The aim of the research is to determine the financial indicators which financial analysts consider when analyzing a loan application that have the most influence on the decision to approve or reject a loan application. The internal determinants of credit risk of agricultural enterprises are analyzed, i.e., indicators of financial leverage, profitability, liquidity, solvency, financial stability and effectiveness. The analyzed models gave different results in significance of the observed indicators. The indicators that stood out as significant in both models are only indicators of profitability and solvency. The model of discriminant analysis has successfully classified rate 81.0%, while the logistic regression model has successfully classifies rate 89.8%. In modeling the credit risk of agricultural enterprises in the Republic of Serbia, the logistic regression model gives better results.

Risk assessment of severe tricyclic antidepressant overdose

2009 ◽  
Vol 28 (8) ◽  
pp. 511-519 ◽  
Author(s):  
Florian Eyer ◽  
Jochen Stenzel ◽  
Tibor Schuster ◽  
Norbert Felgenhauer ◽  
Rudi Pfab ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Logistic Regression ◽  
Regression Model ◽  
Clinical Characteristics ◽  
Logistic Regression Model ◽  
Early Stage ◽  
Statistical Significance ◽  
Time Lag ◽  
Tricyclic Antidepressant ◽  
Multivariable Logistic Regression Model

Prognostic factors for severe complications in tricyclic antidepressant (TCA) overdose remain unclear. We therefore evaluated the value of clinical characteristics and electrocardiograph (ECG) parameters to predict serious events (seizures, arrhythmia, death) in severe TCA overdose of 100 patients using logistic regression models for risk assessment. The overall fatality rate was 6%, arrhythmia occurred in 21% and 31% of the patients developed seizures. Using an univariable logistic regression model, the maximal QRS interval (OR 1.22; 95% CI 1.06-1.41; p = .005), the time lag between ingestion and occurrence of first symptoms of overdose (OR 1.13; 95% CI 0.99-1.29; p = .072) and the age (OR 0.73; 95% CI 0.55-0.98; p = .038) were determined as the solely predictive parameters. In the multivariable logistic regression model, the QRS interval could not be established as independent predictor, however, the terminal 40-ms frontal plane QRS vector (T40) reached statistical significance regarding prediction of serious events (odds ration [OR] 1.70; 95% confidence interval [CI] 1.02-2.84; p = .041), along with age and time lag between ingestion and onset of symptoms of overdose with a sensitivity and specificity of 71% and 70%, respectively. Evaluation of both clinical characteristics and ECG-parameters in the early stage of TCA overdose may help to identify those patients who urgently need further aggressive medical observation and management.

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