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.

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 Credit Scoring for Financial Services Institution using Ant Colony Optimization Algorithm under Logistic Regression Model

2019 ◽  
Vol 8 (4) ◽  
pp. 5957-5961
Keyword(s):  
Logistic Regression ◽  
Credit Risk ◽  
Regression Model ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Logistic Regression Model ◽  
Financial Institution ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Credit Analysis

Economic and trade activities are important in a country. All these activities are regulated by financial institutions, such as banks. The process of channeling funds to the public or known as credit is one of the tasks of the banking sector which aims to improve the people's economy. Credit granting is required for credit analysis, which is useful to determine the level of eligibility of a debtor to receive credit. The function of the credit analysis is to reduce the credit risk of prospective debtors who have failed to pay as well as to avoid financial institution losses or charges. The method used to analyze credit risk in this study is the Ant Colony Optimization algorithm in the Logistic Regression model. Past data held by each prospective debtor obtained from one financial institution in Indonesia is used as a feasibility parameter in this analysis. The results of the study showed that eight variables analyzed were five variables including the significant influence (age of debt ( 1 X ), family dependents ( 2 X ), value of the collection ( 4 X ), the number of credit limits ( 6 X ), and the term of the loan ( 8 X ) while the other three variables (the amount of savings ( 3 X ), income per month ( 5 X ), net income ( 7 X ) are not significant to the risk of default.

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.

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