Visualizing the Function Computed by a Feedforward Neural Network

2000 ◽  
Vol 12 (6) ◽  
pp. 1337-1353 ◽  
Author(s):  
Tony A. Plate ◽  
Joel Bert ◽  
John Grace ◽  
Pierre Band
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Statistical Model ◽  
Neural Network Model ◽  
Feedforward Neural Network ◽  
Visualization Method ◽  
Output Function ◽  
Input Output ◽  
Probabilistic Classification ◽  
Classification Tasks

A method for visualizing the function computed by a feedforward neural network is presented. It is most suitable for models with continuous inputs and a small number of outputs, where the output function is reasonably smooth, as in regression and probabilistic classification tasks. The visualization makes readily apparent the effects of each input and the way in which the functions deviate from a linear function. The visualization can also assist in identifying interactions in the fitted model. The method uses only the input-output relationship and thus can be applied to any predictive statistical model, including bagged and committee models, which are otherwise difficult to interpret. The visualization method is demonstrated on a neural network model of how the risk of lung cancer is affected by smoking and drinking.

scholarly journals Novel sensitivity method for evaluating the first derivative of the feed-forward neural network outputs

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ravi Kiran ◽  
Dayakar L. Naik
Keyword(s):  
Neural Network ◽  
Feedforward Neural Network ◽  
Analytical Quality ◽  
Feed Forward Neural Network ◽  
First Derivative ◽  
Network Output ◽  
Novel Method ◽  
Classification Tasks ◽  
Trained Neural Network ◽  
Sensitivity Method

AbstractEvaluating the exact first derivative of a feedforward neural network (FFNN) output with respect to the input feature is pivotal for performing the sensitivity analysis of the trained neural network with respect to the inputs. In this paper, a novel method is presented that computes the analytical quality first derivative of a trained feedforward neural network output with respect to the input features without the need for backpropagation. To this end, the complex step derivative approximation is illustrated, and its implementation in the framework of the feedforward neural network is described. Artificial datasets are generated, and the efficacy of the proposed method for both regression and classification tasks is demonstrated. The results obtained for the regression task indicated that the proposed method is capable of obtaining analytical quality derivatives, and in the case of the classification task, the least relevant features could be identified.

scholarly journals Prediction of Lung Infection during Palliative Chemotherapy of Lung Cancer Based on Artificial Neural Network

2022 ◽  
Vol 2022 ◽  
pp. 1-7
Author(s):  
Wei Guo ◽  
Guoyun Gao ◽  
Jun Dai ◽  
Qiming Sun
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Cancer Patients ◽  
Network Model ◽  
Neural Network Model ◽  
Palliative Chemotherapy ◽  
Lung Infection ◽  
Lung Cancer Patients

Lung infection seriously affects the effect of chemotherapy in patients with lung cancer and increases pain. The study is aimed at establishing the prediction model of infection in patients with lung cancer during chemotherapy by an artificial neural network (ANN). Based on the data of historical cases in our hospital, the variables were screened, and the prediction model was established. A logistic regression (LR) model was used to screen the data. The indexes with statistical significance were selected, and the LR model and back propagation neural network model were established. A total of 80 cases of advanced lung cancer patients with palliative chemotherapy were predicted, and the prediction performance of different model was evaluated by the receiver operating characteristic curve (ROC). It was found that age ≧ 60 years, length of stay ≧ 14  d, surgery history, combined chemotherapy, myelosuppression, diabetes, and hormone application were risk factors of infection in lung cancer patients during chemotherapy. The area under the ROC curve of the LR model for prediction lung infection was 0.729 ± 0.084 , which was less than that of the ANN model ( 0.897 ± 0.045 ). The results concluded that the neural network model is better than the LR model in predicting lung infection of lung cancer patients during chemotherapy.

scholarly journals Best Paper Selection

2018 ◽  
Vol 27 (01) ◽  
pp. 113-113
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Deep Learning ◽  
Neural Network Model ◽  
Skeletal Maturity ◽  
Low Complexity ◽  
Noise Detection ◽  
Healthcare Monitoring ◽  
Deep Learning Neural Network ◽  
Hand Radiographs

Bote JM, Recas J, Rincon F, Atienza D, Hermida R. A modular low-complexity ECG delineation algorithm for real-time embedded systems. IEEE J Biomed Health Inform 2018;22(2):429-41 https://dx.doi.org/10.1109/JBHI.2017.2671443 Grossmann P, Stringfield O, El-Hachem N, Bui MM, Rios Velazquez E, Parmar C, Leijenaar RT, Haibe-Kains B, Lambin P, Gillies RJ, Aerts HJ. Defining the biological basis of radiomic phenotypes in lung cancer. ELife 2017;6:e23421 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28731408/ Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology 2018; 287(1):313-22 http://pubs.rsna.org/doi/10.1148/radiol.2017170236?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed Satija U, Ramkumar B, Manikandan MS. Automated ECG noise detection and classification system for unsupervised healthcare monitoring. IEEE J Biomed Health Inform 2018;22(3):722-32 https://dx.doi.org/10.1109/JBHI.2017.2686436

Study of the Concrete Block Temperature Intelligent Prediction Model during the Construction

2013 ◽  
Vol 663 ◽  
pp. 68-71
Author(s):  
Kai Jiang ◽  
Yi Hong Zhou ◽  
Yao Ying Huang ◽  
Shao Wu Zhou ◽  
Dan Dan Liu
Keyword(s):  
Neural Network ◽  
Statistical Model ◽  
Prediction Model ◽  
Temperature Variation ◽  
Network Model ◽  
Neural Network Model ◽  
Concrete Block ◽  
The Neural Network ◽  
Concrete Temperature ◽  
Block Temperature

The explicit statistical model of concrete temperature variation is difficult to reasonably reflect the nonlinear relationship between the historical information and future information. This article is based on neural network intelligence tools and uses the neural network model to describe the concrete temperature variation during the construction. The relationships between the concrete temperature and initial temperature (pouring temperature), environmental temperature, the cement hydration heat temperature increase, water cooling effect and other factors are nonlinear. Establishing the neural network model of concrete temperature variation, exploring the historical temperature information could predict the future temperature information. Applying the intelligent prediction model to a construction project shows that when compared with the traditional explicit temperature statistical model, the temperature neural network prediction model established in this paper has obvious simplicity and superiority.

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