scholarly journals Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation

2019 ◽  
Vol Volume 13 ◽  
pp. 421-430 ◽  
Author(s):  
Edsel B Ing ◽  
Neil R Miller ◽  
Angeline Nguyen ◽  
Wanhua Su ◽  
Lulu LCD Bursztyn ◽  
...  
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Giant Cell Arteritis ◽  
Giant Cell ◽  
Prediction Models ◽  
Regression Diagnostic ◽  
Development And Validation

Experimental study on the factors affecting squeak noise occurrence in automotive suspension bushings

2021 ◽  
pp. 095440702110241
Author(s):  
Byunghyun Kang ◽  
Cheol Choi ◽  
Daeun Sung ◽  
Seongho Yoon ◽  
Byoung-Ho Choi
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Influencing Factors ◽  
Frictional Force ◽  
Prediction Models ◽  
Statistical Tests ◽  
Network Models ◽  
Multiple Logistic Regression ◽  
Neural Network Models ◽  
Automotive Suspension

In this study, friction tests are performed, via a custom-built friction tester, on specimens of natural rubber used in automotive suspension bushings. By analyzing the problematic suspension bushings, the eleven candidate factors that influence squeak noise are selected: surface lubrication, hardness, vulcanization condition, surface texture, additive content, sample thickness, thermal aging, temperature, surface moisture, friction speed, and normal force. Through friction tests, the changes are investigated in frictional force and squeak noise occurrence according to various levels of the influencing factors. The degree of correlation between frictional force and squeak noise occurrence with the factors is determined through statistical tests, and the relationship between frictional force and squeak noise occurrence based on the test results is discussed. Squeak noise prediction models are constructed by considering the interactions among the influencing factors through both multiple logistic regression and neural network analysis. The accuracies of the two prediction models are evaluated by comparing predicted and measured results. The accuracies of the multiple logistic regression and neural network models in predicting the occurrence of squeak noise are 88.2% and 87.2%, respectively.

scholarly journals Diagnostic Prediction Models for Cardiovascular Disease Risk using Data Mining Techniques

2020 ◽  
Vol 14 (2) ◽  
pp. 113-121
Author(s):  
Nongyao Nai-arun ◽  
Rungruttikarn Moungmai
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Logistic Regression ◽  
Prediction Models ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Back Propagation ◽  
Risk Groups ◽  
Back Propagation Neural Network ◽  
Number Of Patients

Cardiovascular disease is the top national health problem that leads to a big number of deaths in Thailand. There is still a growing number of patients with the disease. Proactive measures of disease prevention and disease control are searching for risk groups. Therefore, people who are at risk can diagnose and manage themselves to reduce risk factors and adjust their behavior accordingly. For this reason, the idea of diagnostic prediction models for Cardiovascular was conducted. The data of patients from 126 health promoting hospitals and 12 hospitals in Saraburi Province were collected. Then, the analysis was done to establish 6 models namely logistic regression, random forest, back-propagation neural network, decision tree, naïve bayes and K-nearest neighbors. Moreover, 10-fold cross validation was applied into the process of each model. The results revealed that the logistic regression model achieved the highest accuracy rate, 99.940%, followed by the back-propagation neural network model, 98.506%. The best model should be developed as a web application to search for new patients or risk groups. It will help to prevent and control the disease quickly and also to reduce mortality.

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 Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 462
Author(s):  
Katarzyna Zaorska ◽  
Tomasz Szczapa ◽  
Maria Borysewicz-Lewicka ◽  
Michał Nowicki ◽  
Karolina Gerreth
Keyword(s):  
Neural Network ◽  
Early Childhood ◽  
Logistic Regression ◽  
Single Nucleotide Polymorphisms ◽  
Early Childhood Caries ◽  
Prediction Models ◽  
Oral Epithelium ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Childhood Caries

Background: Several genes and single nucleotide polymorphisms (SNPs) have been associated with early childhood caries. However, they are highly age- and population-dependent and the majority of existing caries prediction models are based on environmental and behavioral factors only and are scarce in infants. Methods: We examined 6 novel and previously analyzed 22 SNPs in the cohort of 95 Polish children (48 caries, 47 caries-free) aged 2–3 years. All polymorphisms were genotyped from DNA extracted from oral epithelium samples. We used Fisher’s exact test, receiver operator characteristic (ROC) curve and uni-/multi-variable logistic regression to test the association of SNPs with the disease, followed by the neural network (NN) analysis. Results: The logistic regression (LogReg) model showed 90% sensitivity and 96% specificity, overall accuracy of 93% (p < 0.0001), and the area under the curve (AUC) was 0.970 (95%CI: 0.912–0.994; p < 0.0001). We found 90.9–98.4% and 73.6–87.2% prediction accuracy in the test and validation predictions, respectively. The strongest predictors were: AMELX_rs17878486 and TUFT1_rs2337360 (in both LogReg and NN), MMP16_rs1042937 (in NN) and ENAM_rs12640848 (in LogReg). Conclusions: Neural network prediction model might be a substantial tool for screening/early preventive treatment of patients at high risk of caries development in the early childhood. The knowledge of potential risk status could allow early targeted training in oral hygiene and modifications of eating habits.

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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