Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling – a case study

Purpose. Anthropometry is a useful tool for assessing some risk factors for frailty. Thus, the aim of this study was to verify the discriminatory performance of anthropometric measures in identifying frailty in the elderly and to create an easy-to-use tool. Methods. Cross-sectional study: a subset from the Multidimensional Study of the Elderly in the Family Health Strategy (EMI-SUS) evaluating 538 older adults. Individuals were classified using the Fried Phenotype criteria, and 26 anthropometric measures were obtained. The predictive ability of anthropometric measures in identifying frailty was identified through logistic regression and an artificial neural network. The accuracy of the final models was assessed with an ROC curve. Results. The final model comprised the following predictors: weight, waist circumference, bicipital skinfold, sagittal abdominal diameter, and age. The final neural network models presented a higher ROC curve of 0.78 (CI 95% 0.74–0.82) (P<0.001) than the logistic regression model, with an ROC curve of 0.71 (CI 95% 0.66–0.77) (P<0.001). Conclusion. The neural network model provides a reliable tool for identifying prefrailty/frailty in the elderly, with the advantage of being easy to apply in the primary health care. It may help to provide timely interventions to ameliorate the risk of adverse events.

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Land use change modeling through an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis (case study: Arasbaran region, Iran)

Journal of Forestry Research ◽

10.1007/s11676-018-0659-9 ◽

2018 ◽

Vol 30 (3) ◽

pp. 943-957 ◽

Cited By ~ 15

Author(s):

Vahid Nasiri ◽

Ali. A. Darvishsefat ◽

Reza Rafiee ◽

Anoushirvan Shirvany ◽

Mohammad Avatefi Hemat

Keyword(s):

Neural Network ◽

Land Use ◽

Markov Chain ◽

Land Use Change ◽

Multi Layer Perceptron ◽

Markov Chain Analysis ◽

Land Use Change Modeling ◽

Chain Analysis

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

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211024109 ◽

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.

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