{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T05:10:09Z","timestamp":1745557809214,"version":"3.40.4"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031557286"},{"type":"electronic","value":"9783031557293"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-55729-3_13","type":"book-chapter","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T10:50:56Z","timestamp":1710931856000},"page":"159-171","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Distributed and Collaborative Learning Approach for Stroke Prediction"],"prefix":"10.1007","author":[{"given":"Firas","family":"Aissaoui","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Imen","family":"Boudali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takoua","family":"Abdellatif","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","first-page":"57","DOI":"10.11648\/j.ajtab.20180402.14","volume":"4","author":"SA Senthilkumar","year":"2018","unstructured":"Senthilkumar, S.A., Rai, B.K., Meshram, A.A., Gunasekaran, A., Chandrakumarmangalam, S.: Big data in healthcare management: a review of literature. Am. J. Theor. Appl. Bus. 4, 57\u201369 (2018)","journal-title":"Am. J. Theor. Appl. Bus."},{"issue":"1","key":"13_CR2","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1177\/17474930211065917","volume":"17","author":"VL Feigin","year":"2022","unstructured":"Feigin, V.L., et al.: World Stroke Organization (WSO): global stroke fact sheet. Int. J. Stroke 17(1), 18\u201329 (2022). https:\/\/doi.org\/10.1177\/17474930211065917","journal-title":"Int. J. Stroke"},{"key":"13_CR3","doi-asserted-by":"publisher","unstructured":"Singh, M.S., Choudhary, P.: Stroke prediction using artificial intelligence. In: Proceedings of the 2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), Bangkok, Thailand, pp. 158\u2013161 (2017). https:\/\/doi.org\/10.1109\/IEMECON.2017.8079581","DOI":"10.1109\/IEMECON.2017.8079581"},{"key":"13_CR4","doi-asserted-by":"publisher","unstructured":"Jeena, R.S., Kumar, S.: Stroke prediction using SVM. In: Proceedings of International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 600\u2013602 (2016). https:\/\/doi.org\/10.1109\/ICCICCT.2016.7988020","DOI":"10.1109\/ICCICCT.2016.7988020"},{"issue":"12","key":"13_CR5","doi-asserted-by":"publisher","first-page":"e23440","DOI":"10.2196\/23440","volume":"5","author":"EM Alanazi","year":"2021","unstructured":"Alanazi, E.M., Abdou, A., Luo, J.: Predicting Risk of stroke from lab tests using machine learning algorithms: development and evaluation of prediction models. JMIR Form Res. 5(12), e23440 (2021). https:\/\/doi.org\/10.2196\/23440","journal-title":"JMIR Form Res."},{"key":"13_CR6","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1007\/s00521-019-04041-y","volume":"32","author":"P Govindarajan","year":"2020","unstructured":"Govindarajan, P., Soundarapandian, R.K., Gandomi, A.H., Patan, R., Jayaraman, P., Manikandan, R.: Classification of stroke disease using machine learning algorithms. Neural Comput. Appl. 32, 817\u2013828 (2020). https:\/\/doi.org\/10.1007\/s00521-019-04041-y","journal-title":"Neural Comput. Appl."},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Hung, C.-Y., Chen, W.-C., Lai, P.-T., Lin, C.-H., Lee, C.-C.: Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),pp. 3110\u20133113. IEEE (2017)","DOI":"10.1109\/EMBC.2017.8037515"},{"key":"13_CR8","doi-asserted-by":"publisher","first-page":"1876","DOI":"10.3390\/ijerph16111876","volume":"16","author":"S Cheon","year":"2019","unstructured":"Cheon, S., Kim, J., Lim, J.: The use of deep learning to predict stroke patient mortality. Int. J. Environ. Res. Public Health 16, 1876 (2019)","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"13_CR9","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1007\/978-3-319-96136-1_25","volume-title":"Machine Learning and Data Mining in Pattern Recognition: 14th International Conference, MLDM 2018, New York, NY, USA, July 15-19, 2018, Proceedings, Part I","author":"P Chantamit-o-pas","year":"2018","unstructured":"Chantamit-o-pas, P., Goyal, M.: Long short-term memory recurrent neural network for stroke prediction. In: Perner, P. (ed.) Machine Learning and Data Mining in Pattern Recognition: 14th International Conference, MLDM 2018, New York, NY, USA, July 15-19, 2018, Proceedings, Part I, pp. 312\u2013323. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-96136-1_25"},{"key":"13_CR10","doi-asserted-by":"publisher","unstructured":"Kaur, M., Sakhare, S.-R., Wanjale, K., Akter, F.: Early stroke prediction methods for prevention of strokes. Behav Neuro. Hindawi. (2022). https:\/\/doi.org\/10.1155\/2022\/7725597","DOI":"10.1155\/2022\/7725597"},{"issue":"7","key":"13_CR11","doi-asserted-by":"publisher","first-page":"3500","DOI":"10.3390\/s23073500","volume":"23","author":"BM Elbagoury","year":"2023","unstructured":"Elbagoury, B.M., Vladareanu, L., Vl\u0103d\u0103reanu, V., Salem, A.B., Travediu, A.M., Roushdy, M.I.A.: Hybrid stacked CNN and residual feedback GMDH-LSTM deep learning model for stroke prediction applied on mobile AI smart hospital platform. Sensors. 23(7), 3500 (2023). https:\/\/doi.org\/10.3390\/s23073500","journal-title":"Sensors."},{"key":"13_CR12","unstructured":"Konec\u00eany, J., McMahan, H.B., Yu, F.X., Richtarik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)"},{"key":"13_CR13","unstructured":"Bonawitz K., et al.: Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046 (2019)"},{"key":"13_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10586-022-03658-4","volume":"17","author":"A Rahman","year":"2022","unstructured":"Rahman, A., et al.: Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues. Cluster Comput. 17, 1\u201341 (2022). https:\/\/doi.org\/10.1007\/s10586-022-03658-4","journal-title":"Cluster Comput."},{"key":"13_CR15","unstructured":"Documentations from Tensorflow Keras. https:\/\/www.tensorflow.org"},{"key":"13_CR16","unstructured":"Health Dataset (Stroke Data). https:\/\/www.kaggle.com\/datasets\/prosperchuks\/health-dataset?select=stroke_data.csv"},{"key":"13_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","volume":"5","author":"M Hossin","year":"2015","unstructured":"Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manag. Process 5, 1 (2015)","journal-title":"Int. J. Data Min. Knowl. Manag. Process"}],"container-title":["Communications in Computer and Information Science","Advances in Model and Data Engineering in the Digitalization Era"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-55729-3_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T04:36:19Z","timestamp":1745555779000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-55729-3_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031557286","9783031557293"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-55729-3_13","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"21 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MEDI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Model and Data Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sousse","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tunisia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"medi2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/medi2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}