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 Detection of Lung Cancer using Multilayer Perceptron Neural Network

2017 ◽  
Vol 1 (4) ◽  
pp. 109
Author(s):  
Farzad Mirzakhani
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Artificial Neural Network ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Network Model ◽  
Neural Network Model ◽  
Multilayer Perceptron ◽  
Predictive Value ◽  
Artificial Neural Network Model

Introduction: Lung cancer is the most common cancer in terms of prevalence and mortality. The cancer can be detected once it is reached to a stage that is visible in the CT imaging. Eighty six percent of the patients with lung cancer because they are late understand their disease, surgery has little effect on their improvement. Therefore, the existence of an intelligent system that can detect lung cancer in the early stages is necessary. Methods: In this study, a lung cancer dataset of UCI database was used. This dataset consists of 32 samples, 57 variables and 3 classes (each class including 10, 9 and 13 samples). The data were normalized within the range 0 to 1. Then, to increase the detection speed, the dimensions of the data were reduced by using the Principal Components Analysis (PCA). Then, using a multilayer perceptron neural network, a model for classification and prediction of lung cancer was developed. Finally, the performance of the model was measured using sensitivity, specificity, positive predictive value and negative predictive value. It should be noted that all analyzes were done using Weka software. Results: After developing and evaluating an artificial neural network model, the developed model had a sensitivity of 66.7%, a 98.5% specificity, a positive predictive value of 75%, and a negative predictive value of 97.7%. Conclusion: In intelligent diagnostic systems, in addition to high accuracy of diagnosis, the speed of diagnosis and decision making is also important. Therefore, researchers increased the speed of the prediction model by reducing 57 variables to 8 variables using PCA. Also, the high sensitivity and high specificity of developed model demonstrates high power of artificial neural network model in detecting lung cancer.

Combining the Taguchi Method with an Artificial Neural Network to Construct a Prediction Model for Near-Field Photolithography Experiments

2010 ◽  
Vol 224 (10) ◽  
pp. 2223-2233 ◽  
Author(s):  
Z-C Lin ◽  
C-B Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Taguchi Method ◽  
Prediction Model ◽  
Network Model ◽  
Neural Network Model ◽  
Near Field ◽  
Full Field ◽  
Artificial Neural ◽  
Training Examples

For analysis using the Taguchi method, the L18 or L27 orthogonal array is usually adopted. However, this requires many experiments (18 or 27 runs, respectively), which consumes time and increases costs. In addition, while traditional analysis with the Taguchi model provides a better group of processing parameters, it cannot predict the unexperimented results. This article proposes a progressive Taguchi neural network model that combines the Taguchi method with an artificial neural network and constructs a prediction model for near-field photolithography experiments. This approach establishes a Taguchi neural network that requires fewer experimental runs, while achieving a high predictive precision. The analytical results of the progressive Taguchi neural network model show that, because there are few training examples in the stage 1 preliminary network, there is a significant fluctuation in the network prediction values. In the stage 2 refining network, the prediction effect in the region around the Taguchi factor level points is not bad, but the prediction in the region more remote from the learning and training examples has greater error. The stage 3 precise network can provide optimal prediction results for the full field.

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