scholarly journals Comparison Of Classification Success Of Human Development Index By Using Ordered Logistic Regression Analysis And Artificial Neural Network Methods

2015 ◽  
Vol 7 (4) ◽  
pp. 172-172
Author(s):  
Emre YAKUT ◽  
Murat GÜNDÜZ ◽  
Ayhan Demirci
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Human Development ◽  
Logistic Regression Analysis ◽  
Human Development Index ◽  
Development Index ◽  
Ordered Logistic Regression ◽  
Network Methods

scholarly journals Prediction of prostate cancer by deep learning with multilayer artificial neural network

2018 ◽  
Vol 13 (5) ◽  
Author(s):  
Takumi Takeuchi ◽  
Mami Hattori-Kato ◽  
Yumiko Okuno ◽  
Satoshi Iwai ◽  
Koji Mikami
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Deep Learning ◽  
Logistic Regression Analysis ◽  
Prostate Biopsy ◽  
Stepwise Logistic Regression ◽  
Artificial Neural

Introduction: To predict the rate of prostate cancer detection on prostate biopsy more accurately, the performance of deep learning using a multilayer artificial neural network was investigated. Methods: A total of 334 patients who underwent multiparametric magnetic resonance imaging before ultrasonography guided transrectal 12-core prostate biopsy were enrolled in the analysis. Twenty-two non-selected variables, as well as selected ones by least absolute shrinkage and selection operator (Lasso) regression analysis and by stepwise logistic regression analysis, were input into the constructed multilayer artificial neural network (ANN) programs; 232 patients were used as training cases of ANN programs and the remaining 102 patients were for the test to output the probability of prostate cancer existence, accuracy of prostate cancer prediction, and area under the receiver operating characteristic (ROC) curve with the learned model. Results: With any prostate cancer objective variable, Lasso and stepwise regression analyses selected 12 and nine explanatory variables, respectively, from 22. Using trained ANNs with multiple hidden layers, the accuracy of predicting any prostate cancer in test samples was about 5–10% higher compared to that with logistic regression analysis (LR). The area under the curves (AUC) with multilayer ANN were significantly larger on inputting variables that were selected by the stepwise LR compared with the AUC with LR. The ANN had a higher net benefit than LR between prostate cancer probability cutoff values of 0.38 and 0.6. Conclusions: ANN accurately predicted prostate cancer without biopsy marginally better than LR. However, for clinical application, ANN performance may still need improvement.

scholarly journals Prediction of prostate cancer by deep learning with multilayer artificial neural network

2018 ◽  
Author(s):  
Takumi Takeuchi ◽  
Mami Hattori-Kato ◽  
Yumiko Okuno ◽  
Satoshi Iwai ◽  
Koji Mikami
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Deep Learning ◽  
Logistic Regression Analysis ◽  
Prostate Biopsy ◽  
Stepwise Logistic Regression ◽  
Artificial Neural

AbstractObjectivesTo predict the rate of prostate cancer detection on prostate biopsy more accurately, the performance of deep learning utilizing a multilayer artificial neural network was investigated.Materials and methodsA total of 334 patients who underwent multiparametric magnetic resonance imaging before ultrasonography-guided transrectal 12-core prostate biopsy were enrolled in the analysis. Twenty-two non-selected variables as well as selected ones by least absolute shrinkage and selection operator (Lasso) regression analysis and by stepwise logistic regression analysis were input into the constructed multilayer artificial neural network (ANN) programs. 232 patients were used as training cases of ANN programs, and the remaining 102 patients were for the test to output the probability of prostate cancer existence, accuracy of prostate cancer prediction, and area under the receiver operating characteristic (ROC) curve with the learned model.ResultsWith any prostate cancer objective variable, Lasso and stepwise regression analyses selected 12 and 9 explanatory variables from 22, respectively. In common between them, age at biopsy, findings on digital rectal examination, findings in the peripheral zone on MRI diffusion-weighted imaging, and body mass index were positively influential variables, while numbers of previous prostatic biopsy and prostate volume were negatively influential. Using trained ANNs with multiple hidden layers, the accuracy of predicting any prostate cancer in test samples was about 5-10% higher compared with that with logistic regression analysis (LR). The AUCs with multilayer ANN were significantly larger on inputting variables that were selected by the stepwise logistic regression compared with the AUC with LR. The ANN had a higher net-benefit than LR between prostate cancer probability cut-off values of 0.38 and 0.6.ConclusionANN accurately predicted prostate cancer without biopsy marginally better than LR. However, for clinical application, ANN performance may still need improvement.

scholarly journals Comparison of Artificial Neural Networks and Logistic Regression Analysis in Pregnancy Prediction Using the In Vitro Fertilization Treatment

2013 ◽  
Vol 35 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Robert Milewski ◽  
Anna Justyna Milewska ◽  
Teresa Więsak ◽  
Allen Morgan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Vitro Fertilization ◽  
Multivariable Logistic Regression ◽  
Artificial Neural

Abstract Infertility is recognized as a major problem of modern society. Assisted Reproductive Technology (ART) is the one of many available treatment options to cure infertility. However, the efficiency of the ART treatment is still inadequate. Therefore, the procedure’s quality is constantly improving and there is a need to determine statistical predictors as well as contributing factors to the successful treatment. There is a concern over the application of adequate statistical analysis to clinical data: should classic statistical methods be used or would it be more appropriate to apply advanced data mining technologies? By comparing two statistical models, Multivariable Logistic Regression analysis and Artificial Neural Network it has been demonstrated that Multivariable Logistic Regression analysis is more suitable for theoretical interest but the Artificial Neural Network method is more useful in clinical prediction.

scholarly journals Factors Identification for Human Development Index

2020 ◽  
Vol 2 (1) ◽  
pp. 1-8
Author(s):  
Anam Javaid ◽  
Atif Akbar
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Human Development ◽  
Survey Data ◽  
Human Development Index ◽  
Economic Condition ◽  
Health Index ◽  
Development Index ◽  
Dependent Factor ◽  
Significant Factors

Human development index matters a lot for the economic condition of a country. It can be calculated with the help of the education index, health index, and GDP. So, by looking only at the value of the human development index (HDI), the economy of the country can be judged. The contribution of this research project is the identification of factors related to human development. The factors include the availability of the basic necessities, facilities related to education, health, and income. For this purpose, different important factors have been observed for the Multan district by taking MICS (2007-2008) survey data. Logistic regression is applied for the purpose of analysis. Significant factors are noted in each regression analysis based on the dependent factor.

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