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Because this disease usually does not manifest itself until the kidney is severely damaged, early detection saves many people\u2019s lives. Therefore, the contribution of the current paper is proposing three predictive models to predict CKD possible occurrence within 6 or 12 months before disease existence namely; convolutional neural network (CNN), long short-term memory (LSTM) model, and deep ensemble model. The deep ensemble model fuses three base deep learning classifiers (CNN, LSTM, and LSTM-BLSTM) using majority voting technique. To evaluate the performance of the proposed models, several experiments were conducted on two different public datasets. Among the predictive models and the reached results, the deep ensemble model is superior to all the other models, with an accuracy of 0.993 and 0.992 for the 6-month data and 12-month data predictions, respectively.<\/jats:p>","DOI":"10.1007\/s13755-023-00261-8","type":"journal-article","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T07:03:31Z","timestamp":1701414211000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["Deep-kidney: an effective deep learning framework for chronic kidney disease prediction"],"prefix":"10.1007","volume":"12","author":[{"given":"Dina","family":"Saif","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amany M.","family":"Sarhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nada M.","family":"Elshennawy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,1]]},"reference":[{"key":"261_CR1","doi-asserted-by":"crossref","unstructured":"Barik S, et al. 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