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Predicting Breast Cancer Based on Optimized Deep Learning Approach
Breast cancer is a dangerous disease with a high morbidity and mortality rate. One of the most important aspects in breast cancer treatment is getting an accurate diagnosis. Machine-learning (ML) and deep learning techniques can help doctors in making diagnosis decisions. This paper proposed the opt...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8957426/ https://www.ncbi.nlm.nih.gov/pubmed/35345799 http://dx.doi.org/10.1155/2022/1820777 |
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author | Saleh, Hager Abd-el ghany, Sara F. Alyami, Hashem Alosaimi, Wael |
author_facet | Saleh, Hager Abd-el ghany, Sara F. Alyami, Hashem Alosaimi, Wael |
author_sort | Saleh, Hager |
collection | PubMed |
description | Breast cancer is a dangerous disease with a high morbidity and mortality rate. One of the most important aspects in breast cancer treatment is getting an accurate diagnosis. Machine-learning (ML) and deep learning techniques can help doctors in making diagnosis decisions. This paper proposed the optimized deep recurrent neural network (RNN) model based on RNN and the Keras–Tuner optimization technique for breast cancer diagnosis. The optimized deep RNN consists of the input layer, five hidden layers, five dropout layers, and the output layer. In each hidden layer, we optimized the number of neurons and rate values of the dropout layer. Three feature-selection methods have been used to select the most important features from the database. Five regular ML models, namely decision tree (DT), support vector machine (SVM), random forest (RF), naive Bayes (NB), and K-nearest neighbor algorithm (KNN) were compared with the optimized deep RNN. The regular ML models and the optimized deep RNN have been applied the selected features. The results showed that the optimized deep RNN with the selected features by univariate has achieved the highest performance for CV and the testing results compared to the other models. |
format | Online Article Text |
id | pubmed-8957426 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-89574262022-03-27 Predicting Breast Cancer Based on Optimized Deep Learning Approach Saleh, Hager Abd-el ghany, Sara F. Alyami, Hashem Alosaimi, Wael Comput Intell Neurosci Research Article Breast cancer is a dangerous disease with a high morbidity and mortality rate. One of the most important aspects in breast cancer treatment is getting an accurate diagnosis. Machine-learning (ML) and deep learning techniques can help doctors in making diagnosis decisions. This paper proposed the optimized deep recurrent neural network (RNN) model based on RNN and the Keras–Tuner optimization technique for breast cancer diagnosis. The optimized deep RNN consists of the input layer, five hidden layers, five dropout layers, and the output layer. In each hidden layer, we optimized the number of neurons and rate values of the dropout layer. Three feature-selection methods have been used to select the most important features from the database. Five regular ML models, namely decision tree (DT), support vector machine (SVM), random forest (RF), naive Bayes (NB), and K-nearest neighbor algorithm (KNN) were compared with the optimized deep RNN. The regular ML models and the optimized deep RNN have been applied the selected features. The results showed that the optimized deep RNN with the selected features by univariate has achieved the highest performance for CV and the testing results compared to the other models. Hindawi 2022-03-19 /pmc/articles/PMC8957426/ /pubmed/35345799 http://dx.doi.org/10.1155/2022/1820777 Text en Copyright © 2022 Hager Saleh et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Saleh, Hager Abd-el ghany, Sara F. Alyami, Hashem Alosaimi, Wael Predicting Breast Cancer Based on Optimized Deep Learning Approach |
title | Predicting Breast Cancer Based on Optimized Deep Learning Approach |
title_full | Predicting Breast Cancer Based on Optimized Deep Learning Approach |
title_fullStr | Predicting Breast Cancer Based on Optimized Deep Learning Approach |
title_full_unstemmed | Predicting Breast Cancer Based on Optimized Deep Learning Approach |
title_short | Predicting Breast Cancer Based on Optimized Deep Learning Approach |
title_sort | predicting breast cancer based on optimized deep learning approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8957426/ https://www.ncbi.nlm.nih.gov/pubmed/35345799 http://dx.doi.org/10.1155/2022/1820777 |
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