Comparative study of logistic regression and artificial neural networks on predicting breast cancer cytology
Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in 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...
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| Main Authors: | , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-02-01.
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| Online Access: | Get fulltext |
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| LEADER | 02488 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs22366_14644 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Mohammed, Yosra Abdulaziz |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Saleh, Eman Gadban |e author |
| 245 | 0 | 0 | |a Comparative study of logistic regression and artificial neural networks on predicting breast cancer cytology |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-02-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/22366 | ||
| 520 | |a Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in 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. | ||
| 540 | |a Copyright (c) 2020 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Artificial neural networks; Breast tumor; Classification; Logistic regression; UCI ML repository; Validation | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 21, No 2: February 2021; 1113-1120 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v21.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/22366/14644 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/22366/14644 |z Get fulltext |