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Predicting Breast Cancer Recurrence Using Machine Learning and Deep Learning Models: A Comparative Study

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Al-Azhar University – Gaza
Massa N.M.; Abu-Naser S.S.
Massa, Nawal Maher (60216786700); Abu-Naser, Samy S. (26533902900)
60216786700; 26533902900
2025
Lecture Notes in Networks and Systems
Predicting Breast Cancer Recurrence Using Machine Learning and Deep Learning Models: A Comparative Study
1537 LNNS
183
196
Bhateja V.; Oroumchian F.; Tang J.; Omar Z.
Springer Science and Business Media Deutschland GmbH
Department of Information Technology, Faculty of Engineering and Information Technology, Al-Azhar University, Gaza, Palestine
Massa N.M., Department of Information Technology, Faculty of Engineering and Information Technology, Al-Azhar University, Gaza, Palestine; Abu-Naser S.S., Department of Information Technology, Faculty of Engineering and Information Technology, Al-Azhar University, Gaza, Palestine
S.S. Abu-Naser; Department of Information Technology, Faculty of Engineering and Information Technology, Al-Azhar University, Gaza, Palestine; email: abunaser@alazhar.edu.ps
0
10.1007/978-981-96-9242-2_14
Breast cancer recurrence; Deep learning; Machine learning; METABRIC dataset
Adaptive boosting; Decision making; Diseases; Forecasting; Learning systems; Medical imaging; Oncology; Patient monitoring; Statistical tests; Breast Cancer; Breast cancer recurrence; Cancer recurrence; Deep learning; F1 scores; Learning models; Machine learning models; Machine-learning; METABRIC dataset; Random forests; Deep learning