scholarly journals Comparison of the Performance of Log-logistic Regression and Artificial Neural Networks for Predicting Breast Cancer Relapse

2014 ◽  
Vol 15 (14) ◽  
pp. 5883-5888 ◽  
Author(s):  
Javad Faradmal ◽  
Ali Reza Soltanian ◽  
Ghodratollah Roshanaei ◽  
Reza Khodabakhshi ◽  
Amir Kasaeian
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Cancer Relapse ◽  
Breast Cancer Relapse ◽  
Artificial Neural

Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks

2005 ◽  
Vol 94 (3) ◽  
pp. 265-272 ◽  
Author(s):  
J.M. Jerez ◽  
L. Franco ◽  
E. Alba ◽  
A. Llombart-Cussac ◽  
A. Lluch ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Risk ◽  
Cancer Relapse ◽  
Relapse Prediction ◽  
Breast Cancer Relapse ◽  
Artificial Neural

scholarly journals A Comparison of Artificial Neural Network and Decision Trees with Logistic Regression as Classification Models for Breast Cancer Survival

2020 ◽  
Vol 5 (6) ◽  
pp. 1170-1190
Author(s):  
Venkateswara Rao Mudunuru ◽  
Leslaw A. Skrzypek
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Decision Trees ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Classification Models ◽  
Classification Methods ◽  
Artificial Neural

In the field of medicine, several recent studies have shown the value of Artificial Neural Networks, decision trees, logistic regression are playing a major role as the predictor, and classification methods. The research has been expanded to estimate the incidence of breast, lung, liver, ovarian, cervical, bladder and skin cancer. The main aim of this paper is to develop models of logistic regression, Artificial Neural Networks, and Decision trees using the same input and output variables and to compare their success in predicting breast cancer survival in woman. To find the best model for breast cancer survival, the sensitivity and specificity of all these models are measured and evaluated with their respective confidence intervals and the ROC values.

scholarly journals Comparative study of logistic regression and artificial neural networks on predicting breast cancer cytology

2021 ◽  
Vol 21 (2) ◽  
pp. 1113
Author(s):  
Yosra Abdulaziz Mohammed ◽  
Eman Gadban Saleh
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Performance Metrics ◽  
Artificial Neural ◽  
Sensitivity Specificity ◽  
Receiver Operating

<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver operating characteristic ROC).   Dataset was downloaded from UCI ml repository; it is composed of 9 attributes and 699 samples. The findings are clearly showing that the RBF NN classifier is the best in prediction of the type of breast tumors since it had recorded the highest performance in terms of correct classification rate (accuracy), sensitivity, specificity, and AUC (area under Receiver Operating Characteristic ROC) among all other models.</p>

Artificial Neural Networks Applied to Survival Prediction in Breast Cancer

Oncology ◽  
1999 ◽  
Vol 57 (4) ◽  
pp. 281-286 ◽  
Author(s):  
M. Lundin ◽  
J. Lundin ◽  
H.B. Burke ◽  
S. Toikkanen ◽  
L. Pylkkänen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Survival Prediction ◽  
Artificial Neural

Comparison of Artificial Neural Networks with Logistic Regression for Detection of Obesity

2011 ◽  
Vol 36 (4) ◽  
pp. 2449-2454 ◽  
Author(s):  
Seyed Taghi Heydari ◽  
Seyed Mohammad Taghi Ayatollahi ◽  
Najaf Zare
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Artificial Neural
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