Prognostic accuracy of an artificial neural network in patients undergoing radical cystectomy for bladder cancer: a comparison with logistic regression analysis

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.

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

10.1101/291609 ◽

2018 ◽

Cited By ~ 3

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.

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

Studies in Logic Grammar and Rhetoric ◽

10.2478/slgr-2013-0033 ◽

2013 ◽

Vol 35 (1) ◽

pp. 39-48 ◽

Cited By ~ 8

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.

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Correction: Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

PLoS ONE ◽

10.1371/journal.pone.0176771 ◽

2017 ◽

Vol 12 (4) ◽

pp. e0176771

Author(s):

Zi-Hui Tang ◽

Juanmei Liu ◽

Fangfang Zeng ◽

Zhongtao Li ◽

Xiaoling Yu ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Logistic Regression ◽

Regression Analysis ◽

Prediction Model ◽

Autonomic Dysfunction ◽

Logistic Regression Analysis ◽

Cardiovascular Autonomic Dysfunction ◽

Artificial Neural

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Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

PLoS ONE ◽

10.1371/journal.pone.0070571 ◽

2013 ◽

Vol 8 (8) ◽

pp. e70571 ◽

Cited By ~ 16

Author(s):

Zi-Hui Tang ◽

Juanmei Liu ◽

Fangfang Zeng ◽

Zhongtao Li ◽

Xiaoling Yu ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Logistic Regression ◽

Regression Analysis ◽

Prediction Model ◽

Autonomic Dysfunction ◽

Logistic Regression Analysis ◽

Cardiovascular Autonomic Dysfunction ◽

Artificial Neural

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Research on the Influential Factors of Customer Satisfaction for Hotels: The Artificial Neural Network Approach and Logistic Regression Analysis

Advances in Intelligent Systems and Computing - Proceedings of the 2012 International Conference of Modern Computer Science and Applications ◽

10.1007/978-3-642-33030-8_71 ◽

2013 ◽

pp. 441-448

Author(s):

Han-Chen Huang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Logistic Regression ◽

Regression Analysis ◽

Customer Satisfaction ◽

Logistic Regression Analysis ◽

Influential Factors ◽

Network Approach ◽

Neural Network Approach ◽

Artificial Neural Network Approach

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ON COMPARING MULTI-LAYER PERCEPTRON AND LOGISTIC REGRESSION FOR CLASSIFICATION OF DIABETIC PATIENTS IN FEDERAL MEDICAL CENTER YOLA, ADAMAWA STATE

International Journal of Engineering Technologies and Management Research ◽

10.29121/ijetmr.v8.i6.2021.961 ◽

2021 ◽

Vol 8 (6) ◽

pp. 24-40

Author(s):

H. Ahmed ◽

M. B. Mohammed ◽

I. A. Baba

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Logistic Regression ◽

Regression Analysis ◽

Medical Center ◽

Oral Glucose ◽

Diabetic Patients ◽

Data Set ◽

Artificial Neural

The logistic regression (LR) and Multi-Layer (MLP) are used to handle regression analysis when the dependent response variable is categorical. Therefore, this study assesses the performance of LR and MLP in terms of classification of object/observations into identified component/groups. A data set consists of 553 cases of diabetes were collected at Federal Medical Center, . The variables measured: Age(years), Mass of a patient(kg/meters), glucose level (plasma glucose concentration, a 2-hour in an oral glucose tolerance test), pressure (Diastolic blood pressure ), insulin (2-hour serum insulin mu U/ml) and class variable (0 or 1) treating 0 as false or negative and 1 treated as true or positive test for diabetes. The method used in the study is Logistic regression analysis and the multi-Layer , a type of Artificial Neural Network, confusion matrix, classification, network algorithm and SPSS version 21 for Windows 10.1. The result of the study showed that LP classifies diabetic patients correctly with 91.8% accuracy. it classifies non-diabetic patients with 89.1% accuracy. MLP classifies diabetic patients with 88.6% accuracy while it classifies non-diabetic patients with 93.2% classification accuracy. Overall, MLP classifies better with 91% accuracy while LR classifies with 90.6% accuracy. This study complements other where MLP, a type Artificial neural network classifies and predicts better than other non-neural network classifiers.

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