scholarly journals The usage of logistic regression and artificial neural networks for evaluation and predicting property-liability insurers' solvency in Egypt

2021 ◽  
Vol 1 (3) ◽  
pp. 215-234
Author(s):  
Eid Elghaly Hassan ◽  
◽  
Diping Zhang ◽  
Keyword(s):  
Logistic Regression ◽  
Financial Performance ◽  
Prediction Models ◽  
Retention Rate ◽  
Financial Ratios ◽  
Insurance Companies ◽  
Fiscal Year ◽  
Stepwise Logistic Regression ◽  
Favorable Result ◽  
Artificial Neural

<abstract> <p>Unlike prior solvency prediction studies conducted in Egypt, this study aims to set up a real picture of companies' financial performance in the Egyptian insurance market. Therefore, 11 financial ratios commonly used by NAIC, AM BEST Company, and S &amp; P Global Ratings were calculated for all property-liability insurance companies in Egypt from 2010 to 2020. They have been used to measure those companies' financial performance efficiency levels by comparing these ratios with the international standard limits. The financial analysis results for those companies revealed that property-liability insurers in Egypt do not have the same level of financial performance efficiency where those companies are classified into three groups: excellent, good, and poor. Furthermore, this paper investigates using the stepwise logistic regression model to determine the most factors among these selected financial ratios that influence those companies' financial performance. The results suggest that only three ratios were statistically significant predictors: "Risk retention rate", "Insurance account receivable to total assets", and "Net profit after tax to total assets". Finally, this paper presents the multi-layers artificial neural network with a backpropagation algorithm as a new solvency prediction model with perfect classifying accuracy of 100%. The trained ANN could predict the next fiscal year with a prediction accuracy of 91.67%, and this percent is a good and favorable result comparing to other solvency prediction models used in Egypt.</p> </abstract>

Machine Learning Dimensionality Reduction Showed Marginal Performance Benefit Over Stepwise Regression for Risk Stratification of Chest Pain Patients in the Emergency Department

2020 ◽  
Author(s):  
Nan Liu ◽  
Marcel Lucas Chee ◽  
Zhi Xiong Koh ◽  
Su Li Leow ◽  
Andrew Fu Wah Ho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Chest Pain ◽  
Risk Stratification ◽  
Dimensionality Reduction ◽  
Prediction Models ◽  
Stepwise Logistic Regression ◽  
Stepwise Variable Selection ◽  
Reduction Methods ◽  
Pain Patients

Abstract Background: Chest pain is among the most common presenting complaints in the emergency department (ED). Swift and accurate risk stratification of chest pain patients in the ED may improve patient outcomes and reduce unnecessary costs. Traditional logistic regression with stepwise variable selection has been used to build risk prediction models for ED chest pain patients. In this study, we aimed to investigate if machine learning dimensionality reduction methods can achieve superior performance than the stepwise approach in deriving risk stratification models. Methods: A retrospective analysis was conducted on the data of patients >20 years old who presented to the ED of Singapore General Hospital with chest pain between September 2010 and July 2015. Variables used included demographics, medical history, laboratory findings, heart rate variability (HRV), and HRnV parameters calculated from five to six-minute electrocardiograms (ECGs). The primary outcome was 30-day major adverse cardiac events (MACE), which included death, acute myocardial infarction, and revascularization. Candidate variables identified using univariable analysis were then used to generate the stepwise logistic regression model and eight machine learning dimensionality reduction prediction models. A separate set of models was derived by excluding troponin. Receiver operating characteristic (ROC) and calibration analysis was used to compare model performance.Results: 795 patients were included in the analysis, of which 247 (31%) met the primary outcome of 30-day MACE. Patients with MACE were older and more likely to be male. All eight dimensionality reduction methods marginally but non-significantly outperformed stepwise variable selection; The multidimensional scaling algorithm performed the best with an area under the curve (AUC) of 0.901. All HRnV-based models generated in this study outperformed several existing clinical scores in ROC analysis.Conclusions: HRnV-based models using stepwise logistic regression performed better than existing chest pain scores for predicting MACE, with only marginal improvements using machine learning dimensionality reduction. Moreover, traditional stepwise approach benefits from model transparency and interpretability; in comparison, machine learning dimensionality reduction models are black boxes, making them difficult to explain in clinical practice.

Serum SHBG Is Associated With the Development and Regression of Nonalcoholic Fatty Liver Disease: A Prospective Study

2019 ◽  
Vol 105 (3) ◽  
pp. e791-e804
Author(s):  
Xu Wang ◽  
Jiewen Xie ◽  
Juan Pang ◽  
Hanyue Zhang ◽  
Xu Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Liver Disease ◽  
Fatty Liver ◽  
Nonalcoholic Fatty Liver Disease ◽  
Fatty Liver Disease ◽  
Prediction Models ◽  
Stepwise Logistic Regression ◽  
Stepwise Logistic Regression Analysis ◽  
Nonalcoholic Fatty Liver ◽  
Potential Biomarker

Abstract Context SHBG, a homodimeric glycoprotein produced by hepatocytes has been shown to be associated with metabolic disorders. Whether circulating SHBG levels are predictive of later risk of nonalcoholic fatty liver disease (NAFLD) remains unknown. In this study, we prospectively investigated the association between SHBG and NAFLD progression through a community-based cohort comprising 3389 Chinese adults. Methods NAFLD was diagnosed using abdominal ultrasonography. Serum SHBG levels were measured by chemiluminescent enzyme immunometric assay, and their relationship with NAFLD development and regression was investigated after a mean follow-up of 3.09 years using multivariable logistic regression. Results Basal SHBG was negatively associated with NAFLD development, with a fully adjusted odds ratio (OR) and its 95% confidence interval (CI) of 0.22 (0.12-0.40) (P &lt; .001). In contrast, basal SHBG was positively associated with NAFLD regression, with a fully adjusted OR of 4.83 (2.38-9.81) (P &lt; .001). Multiple-stepwise logistic regression analysis showed that SHBG concentration was an independent predictor of NAFLD development (OR, 0.28 [0.18-0.45]; P &lt; .001) and regression (OR, 3.89 [2.43-6.22]; P &lt; .001). In addition, the area under the receiver operating characteristic curves were 0.764 (95% CI, 0.740-0.787) and 0.762 (95% CI, 0.738-0.785) for the prediction models of NAFLD development and regression, respectively. Conclusions Serum SHBG concentration is associated with the development and regression of NAFLD; moreover, it can be a potential biomarker for predicting NAFLD progression, and also a novel preventive and therapeutic target for NAFLD.

Comparison of Logistic Regression and Artificial Neural Network based Bankruptcy Prediction Models

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Easwaran Iyer ◽  
Vinod Kumar Murti
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Manufacturing Companies ◽  
Recent Developments ◽  
Artificial Neural

Logistic Regression is one of the popular techniques used for bankruptcy prediction and its popularity is attributed due to its robust nature in terms of data characteristics. Recent developments have explored Artificial Neural Networks for bankruptcy prediction. In this study, a paired sample of 174 cases of Indian listed manufacturing companies have been used for building bankruptcy prediction models based on Logistic Regression and Artificial Neural Networks. The time period of study was year 2000 through year 2009. The classification accuracies have been compared for built models and for hold-out sample of 44 paired cases. In analysis and hold-out samples, both the models have shown appreciable classification results, three years prior to bankruptcy. Thus, both the models can be used (by banks, SEBI etc.) for bankruptcy prediction in Indian Context, however, Artificial Neural Network has shown marginal supremacy over Logistic Regression.

scholarly journals Prognostic nomogram for 30-day mortality of deep vein thrombosis patients in intensive care unit

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Runnan Shen ◽  
Ming Gao ◽  
Yangu Tao ◽  
Qinchang Chen ◽  
Guitao Wu ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Deep Vein Thrombosis ◽  
Prediction Models ◽  
Stepwise Logistic Regression ◽  
Internal Validation ◽  
Vein Thrombosis ◽  
New Models ◽  
Deep Vein

Abstract Background We aimed to use the Medical Information Mart for Intensive Care III database to build a nomogram to identify 30-day mortality risk of deep vein thrombosis (DVT) patients in intensive care unit (ICU). Methods Stepwise logistic regression and logistic regression with least absolute shrinkage and selection operator (LASSO) were used to fit two prediction models. Bootstrap method was used to perform internal validation. Results We obtained baseline data of 535 DVT patients, 91 (17%) of whom died within 30 days. The discriminations of two new models were better than traditional scores. Compared with simplified acute physiology score II (SAPSII), the predictive abilities of two new models were improved (Net reclassification improvement [NRI] > 0; Integrated discrimination improvement [IDI] > 0; P < 0.05). The Brier scores of two new models in training set were 0.091 and 0.108. After internal validation, corrected area under the curves for two models were 0.850 and 0.830, while corrected Brier scores were 0.108 and 0.114. The more concise model was chosen to make the nomogram. Conclusions The nomogram developed by logistic regression with LASSO model can provide an accurate prognosis for DVT patients in ICU.

scholarly journals Failure prediction of government funded start-up firms

2017 ◽  
Vol 14 (2) ◽  
pp. 296-306 ◽  
Author(s):  
Oliver Lukason ◽  
Kaspar Käsper
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Financial Development ◽  
Logistic Regression Analysis ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Development Patterns ◽  
Start Up

This study aims to create a prediction model that would forecast the bankruptcy of government funded start-up firms (GFSUs). Also, the financial development patterns of GFSUs are outlined. The dataset consists of 417 Estonian GFSUs, of which 75 have bankrupted before becoming five years old and 312 have survived for five years. Six financial ratios have been calculated for one (t+1) and two (t+2) years after firms have become active. Weighted logistic regression analysis is applied to create the bankruptcy prediction models and consecutive factor and cluster analyses are applied to outline the financial patterns. Bankruptcy prediction models obtain average classification accuracies, namely 63.8% for t+1 and 67.8% for t+2. The bankrupt firms are distinguished with a higher accuracy than the survived firms, with liquidity and equity ratios being the useful predictors of bankruptcy. Five financial patterns are detected for GFSUs, but bankrupt GFSUs do not follow any distinct patterns that would be characteristic only to them.

scholarly journals Establishment of prediction models for lung cancer NOG/PDX models: A guideline for machine learning in small biomedical datasets

2020 ◽  
Author(s):  
Haoyue Guo ◽  
Li Diao ◽  
Hui Qi ◽  
Chunlei Dai ◽  
Yu Chen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Logistic Regression ◽  
Prediction Models ◽  
Gene Mutations ◽  
Gradient Boosting ◽  
Support Vector ◽  
Stepwise Logistic Regression ◽  
Extreme Gradient Boosting ◽  
Nog Mice ◽  
Pdx Models

Abstract Background: Targeted therapy and immune checkpoint inhibitors are the most promising treatments for lung cancers but still facing multiple challenges, including resistance and individual difference. Therefore, patient-derived tumor xenografts (PDX) models are developed for drug discovery and screening. NOG mice is under the destruction of the interleukin-2 (IL-2) receptor common gamma chain, which is appropriate for building PDX models to test immunotherapies. However, current studies have little understanding of the causes of genotype mismatches in PDX or NOG/PDX models, which leads to a massive economic and time loss.Methods: Lung cancer tissues from 53 patients were obtained and engrafted into NOG mice. All of the patients' tumors and NOG/PDX models were detected for common gene mutations. Seventeen clinicopathological features were organized and input to stepwise logistic regression based on the lowest Akaike information criterion (AIC), least absolute shrinkage and selection operator (LASSO)-logistic regression, support vector machine recursive feature elimination (SVM-RFE), eXtreme Gradient Boosting (XGBoost), Gradient Boosting & Categorical Features (CatBoost), and synthetic minority over-sampling technique (SMOTE). Finally, the performance of all models was evaluated by the accuracy, area under the receiver operating characteristic curve (AUC), and F1 score in 100 testing groups.Results: Fifty-three lung cancer NOG/PDX models were successfully established, with a genotype matching rate of 79.2% (42/53). Two multivariable logistic regressions revealed that age, the number of driver mutations, epidermal growth factor receptor (EGFR) gene mutations, the type of prior chemotherapy, prior tyrosine kinase inhibitors (TKIs) therapy, and the source were potent predictors. Moreover, CatBoost (mean accuracy=0.960; mean AUC=0.939; mean F1 score=0.908) and 8-feature SVM (mean accuracy=0.950; mean AUC=0.934; mean F1 score=0.903) showed the best performance compared with the other algorithms. Moreover, the combination of SMOTE with SVM significantly improved the predictive capability (mean accuracy: 0.961 vs. 0.958, P=0.025; mean AUC: 0.940 vs. 0.935, P=0.045; mean F1 score: 0.909 vs. 0.903, P=0.047).Conclusions: We established an optimal predictive model to screen lung cancer patients for NOG/PDX models, and also offered a general approach for building prediction models in small unbalanced biomedical samples.

scholarly journals Predicting 30-day Hospital Readmissions Using Artificial Neural Networks with Medical Code Embedding

2019 ◽  
Author(s):  
Wenshuo Liu ◽  
Karandeep Singh ◽  
Andrew M. Ryan ◽  
Devraj Sukul ◽  
Elham Mahmoudi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Claims Data ◽  
Prediction Models ◽  
Hospital Performance ◽  
Gradient Boosting ◽  
Administrative Claims ◽  
Hierarchical Logistic Regression ◽  
Artificial Neural

ABSTRACTReducing unplanned readmissions is a major focus of current hospital quality efforts. In order to avoid unfair penalization, administrators and policymakers use prediction models to adjust for the performance of hospitals from healthcare claims data. Regression-based models are a commonly utilized method for such risk-standardization across hospitals; however, these models often suffer in accuracy. In this study we, compare four prediction models for unplanned patient readmission for patients hospitalized with acute myocardial infarction (AMI), congestive health failure (HF), and pneumonia (PNA) within the Nationwide Readmissions Database in 2014. We evaluated hierarchical logistic regression and compared its performance with gradient boosting and two models that utilize artificial neural network. We show that unsupervised Global Vector for Word Representations embedding representations of administrative claims data combined with artificial neural network classification models significantly improves prediction of 30-day readmission. Our best models increased the AUC for prediction of 30-day readmissions from 0.68 to 0.72 for AMI, 0.60 to 0.64 for HF, and 0.63 to 0.68 for PNA compared to hierarchical logistic regression. Furthermore, risk-standardized hospital readmission rates calculated from our artificial neural network model that employed embeddings led to reclassification of approximately 10% of hospitals across categories of hospital performance. This finding suggests that prediction models that incorporate new methods classify hospitals differently than traditional regression-based approaches and that their role in assessing hospital performance warrants further investigation.

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