{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T04:09:44Z","timestamp":1781323784846,"version":"3.54.1"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T00:00:00Z","timestamp":1678924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Because of technological advancements and their use in the medical area, many new methods and strategies have been developed to address complex real-life challenges. Breast cancer, a particular kind of tumor that arises in breast cells, is one of the most prevalent types of cancer in women and is. Early breast cancer detection and classification are crucial. Early detection considerably increases the likelihood of survival, which motivates us to contribute to different detection techniques from a technical standpoint. Additionally, manual detection requires a lot of time and effort and carries the risk of pathologist error and inaccurate classification. To address these problems, in this study, a hybrid deep learning model that enables decision making based on data from multiple data sources is proposed and used with two different classifiers. By incorporating multi-omics data (clinical data, gene expression data, and copy number alteration data) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, the accuracy of patient survival predictions is expected to be improved relative to prediction utilizing only one modality of data. A convolutional neural network (CNN) architecture is used for feature extraction. LSTM and GRU are used as classifiers. The accuracy achieved by LSTM is 97.0%, and that achieved by GRU is 97.5, while using decision fusion (LSTM and GRU) achieves the best accuracy of 98.0%. The prediction performance assessed using various performance indicators demonstrates that our model outperforms currently used methodologies.<\/jats:p>","DOI":"10.3390\/bdcc7010050","type":"journal-article","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T03:14:35Z","timestamp":1678936475000},"page":"50","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Hybrid Deep Learning Framework with Decision-Level Fusion for Breast Cancer Survival Prediction"],"prefix":"10.3390","volume":"7","author":[{"given":"Nermin Abdelhakim","family":"Othman","sequence":"first","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, Helwan University, Cairo 11795, Egypt"},{"name":"Faculty of Informatics and Computer Science, British University in Egypt, Cairo 11837, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2888-0367","authenticated-orcid":false,"given":"Manal A.","family":"Abdel-Fattah","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, Helwan University, Cairo 11795, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahlam Talaat","family":"Ali","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, Helwan University, Cairo 11795, Egypt"},{"name":"Faculty of Computer Science, Nahda University, Beni Suef 62521, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,16]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Improving the accuracy of medical diagnosis with causal machine learning","volume":"11","author":"Richens","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21551","article-title":"Cancer statistics, 2019","volume":"69","author":"Siegel","year":"2019","journal-title":"CA A Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5025","DOI":"10.1109\/JBHI.2022.3187765","article-title":"A Deep Learning Method for Breast Cancer Classification in the Pathology Images","volume":"26","author":"Liu","year":"2022","journal-title":"IEEE J. Biomed. Heal. Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"709027","DOI":"10.3389\/fgene.2021.709027","article-title":"A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients","volume":"12","author":"Guo","year":"2021","journal-title":"Front. Genet."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1007\/s11045-020-00756-7","article-title":"Diagnosis of breast cancer based on modern mammography using hybrid transfer learning","volume":"32","author":"Khamparia","year":"2021","journal-title":"Multidimens. Syst. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1109\/TCBB.2020.3018467","article-title":"Multi-modal classification for human breast cancer prognosis prediction: Proposal of deep-learning based stacked ensemble model","volume":"19","author":"Arya","year":"2020","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9025470","DOI":"10.1155\/2021\/9025470","article-title":"Deep learning in cancer diagnosis and prognosis prediction: A minireview on challenges, recent trends, and future directions","volume":"2021","author":"Tufail","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1049\/cmu2.12269","article-title":"3D convolutional neural networks based automatic modulation classification in the presence of channel noise","volume":"16","author":"Khan","year":"2022","journal-title":"IET Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2021\/6013448","article-title":"Diagnosis of Diabetic Retinopathy through Retinal Fundus Images and 3D Convolutional Neural Networks with Limited Number of Samples","volume":"2021","author":"Tufail","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kamruzzaman, M. (2020, January 6\u201310). Architecture of Smart Health Care System Using Artificial Intelligence. Proceedings of the 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), London, UK.","DOI":"10.1109\/ICMEW46912.2020.9106026"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"012055","DOI":"10.1088\/1757-899X\/853\/1\/012055","article-title":"A review of artificial intelligence techniques for selection & evaluation","volume":"853","author":"Ahmad","year":"2020","journal-title":"IOP Conf. Series: Mater. Sci. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1038\/s41588-018-0295-5","article-title":"A primer on deep learning in genomics","volume":"51","author":"Zou","year":"2019","journal-title":"Nat. Genet."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.irbm.2020.12.002","article-title":"A Hybrid Deep Learning Model for Predicting Molecular Subtypes of Human Breast Cancer Using Multimodal Data","volume":"43","author":"Liu","year":"2021","journal-title":"Irbm"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1162\/neco_a_01273","article-title":"A Survey on Deep Learning for Multimodal Data Fusion","volume":"32","author":"Gao","year":"2020","journal-title":"Neural Comput."},{"key":"ref_15","unstructured":"Nazari, E., Chang, H.-C.H., Deldar, K., Pour, R., Avan, A., Tara, M., Mehrabian, A., and Tabesh, H. (2020). A comprehensive Overview of Decision Fusion Technique in Healthcare: A Systematic Scoping Review. Iran. Red Crescent Med. J., 22."},{"key":"ref_16","first-page":"319","article-title":"DAN: Breast Cancer Classification from High-Resolution Histology Images Using Deep","volume":"1189","author":"Sanyal","year":"2020","journal-title":"Atten. Netw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.ins.2018.12.089","article-title":"Classification of breast cancer histology images using incremental boosting convolution networks","volume":"482","author":"Vo","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106965","DOI":"10.1016\/j.knosys.2021.106965","article-title":"Multi-modal advanced deep learning architectures for breast cancer survival prediction","volume":"221","author":"Arya","year":"2021","journal-title":"Knowledge-Based Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Khanna, A., Polkowski, Z., and Castillo, O. (2022). Data Analytics and Management, Springer Nature Singapore.","DOI":"10.1007\/978-981-19-7615-5"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1007\/s10278-021-00442-5","article-title":"HER2 Molecular Marker Scoring Using Transfer Learning and Decision Level Fusion","volume":"34","author":"Tewary","year":"2021","journal-title":"J. Digit. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.ymeth.2019.06.014","article-title":"Breast cancer histopathological image classification using a hybrid deep neural network","volume":"173","author":"Yan","year":"2019","journal-title":"Methods"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, X., Ahmad, I., Javeed, D., Zaidi, S.A., Alotaibi, F.M., and Ghoneim, M.E. (2022). Intelligent Hybrid Deep Learning Model for Breast Cancer Detection. Electronics, 11.","DOI":"10.3390\/electronics11172767"},{"key":"ref_23","first-page":"12","article-title":"Densely Convolutional Networks for Breast Cancer Classification with Multi-modal Image Fusion","volume":"19","author":"Hamdy","year":"2022","journal-title":"Int. Arab. J. Inf. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105361","DOI":"10.1016\/j.cmpb.2020.105361","article-title":"Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks","volume":"190","author":"Moon","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"14583","DOI":"10.1007\/s00521-021-06099-z","article-title":"A novel feature selection framework based on grey wolf optimizer for mammogram image analysis","volume":"33","author":"Sathiyabhama","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.patcog.2018.05.014","article-title":"Deep learning for image-based cancer detection and diagnosis\u2212A survey","volume":"83","author":"Hu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_27","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13). Understanding the Difficulty of Training Deep Feedforward Neural Networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Sardinia, Italy."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","article-title":"Brain tumor segmentation with Deep Neural Networks","volume":"35","author":"Havaei","year":"2017","journal-title":"Med Image Anal."},{"key":"ref_29","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"163","DOI":"10.2478\/acss-2020-0018","article-title":"Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN","volume":"25","author":"Dutta","year":"2020","journal-title":"Appl. Comput. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/9299621","article-title":"A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis","volume":"2022","author":"Begum","year":"2022","journal-title":"Complexity"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ghosh, P., Azam, S., Hasib, K.M., Karim, A., Jonkman, M., and Anwar, A. (2021, January 18\u201322). A Performance Based Study on Deep Learning Algorithms in the Effective Prediction of Breast Cancer. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China.","DOI":"10.1109\/IJCNN52387.2021.9534293"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1049\/cit2.12061","article-title":"Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification","volume":"6","author":"Jafarbigloo","year":"2021","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_34","first-page":"1","article-title":"A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection","volume":"2022","author":"Ahmad","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shah, A.A., Alturise, F., Alkhalifah, T., and Khan, Y.D. (2022). Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms231911539"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Umer, M., Naveed, M., Alrowais, F., Ishaq, A., Hejaili, A.A., and Alsubai, S. (2022). Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm. Cancers, 14.","DOI":"10.3390\/cancers14236015"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1038\/nature10983","article-title":"The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups","volume":"486","author":"Curtis","year":"2012","journal-title":"Nature"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1007\/s10462-019-09709-4","article-title":"Missing value imputation: A review and analysis of the literature (2006\u20132017)","volume":"53","author":"Lin","year":"2020","journal-title":"Artif. Intell. Rev."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/1\/50\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:56:24Z","timestamp":1760122584000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/1\/50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,16]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["bdcc7010050"],"URL":"https:\/\/doi.org\/10.3390\/bdcc7010050","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,16]]}}}