Predicting post-treatment survivability of patients with breast cancer using Artificial Neural Network methods

2013 ◽  
Author(s):  
Tan-Nai Wang ◽  
Chung-Hao Cheng ◽  
Hung-Wen Chiu
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Post Treatment ◽  
Network Methods ◽  
Artificial Neural

Breast Cancer Classification Using Artificial Neural Network and Feature Selection

2019 ◽  
Vol 8 (2) ◽  
pp. 113
Author(s):  
Frisca Olivia Gorianto ◽  
I Gede Santi Astawa
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Feed Forward ◽  
Network Methods ◽  
Before And After ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Elman Recurrent Neural Network

Breast cancer is still one of the leading causes of death in the world. Prevention can be done if the cancer can be recognized early on whether the cancer is malignant or benign. In this study, a comparison of malignant and benign cancer classifications was performed using two artificial neural network methods, which are the Feed-Forward Backpropagation method and the Elman Recurrent Neural Network method, before and after the feature selection of the data. The result of the study produced that Feed-Forward Backpropagation method using 2 hidden layers is better after the feature selection was performed on the data with an accuracy value of 99,26%.

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

Logistic Regression and Artificial Neural Network: A Comparative Study in Diagnosing Breast Cancer

2021 ◽  
pp. 6330-6334
Author(s):  
W. Abdul Hameed ◽  
Anuradha D. ◽  
Kaspar S.
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Breast Tumor ◽  
Diagnostic Performance ◽  
Malignant Breast Tumor ◽  
Malignant Breast ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Breast tumor is a common problem in gynecology. A reliable test for preoperative discrimination between benign and malignant breast tumor is highly helpful for clinicians in culling the malignant cells through felicitous treatment for patients. This paper is carried out to generate and estimate both logistic regression technique and Artificial Neural Network (ANN) technique to predict the malignancy of breast tumor, utilizing Wisconsin Diagnosis Breast Cancer Database (WDBC). Our aim in this Paper is: (i) to compare the diagnostic performance of both methods in distinguishing between malignant and benign patterns, (ii) to truncate the number of benign cases sent for biopsy utilizing the best model as an auxiliary implement, and (iii) to authenticate the capability of each model to recognize incipient cases as an expert system.

Artificial neural network-based exploration of gene-nutrient interactions in folate and xenobiotic metabolic pathways that modulate susceptibility to breast cancer

Gene ◽  
2016 ◽  
Vol 580 (2) ◽  
pp. 159-168 ◽  
Author(s):  
Shaik Mohammad Naushad ◽  
M. Janaki Ramaiah ◽  
Manickam Pavithrakumari ◽  
Jaganathan Jayapriya ◽  
Tajamul Hussain ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Metabolic Pathways ◽  
Nutrient Interactions ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document