{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:39:16Z","timestamp":1767339556285,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031518485"},{"type":"electronic","value":"9783031518492"}],"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\/978-3-031-51849-2_10","type":"book-chapter","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T05:16:28Z","timestamp":1706764588000},"page":"151-160","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["XGBoost Algorithm to Predict a Patient\u2019s Risk of Stroke"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9207-4335","authenticated-orcid":false,"given":"Sada","family":"Anne","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0568-729X","authenticated-orcid":false,"given":"Amadou Dahirou","family":"Gueye","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,2]]},"reference":[{"issue":"1","key":"10_CR1","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/TBCAS.2021.3137646","volume":"16","author":"G Sivapalan","year":"2022","unstructured":"Sivapalan, G., Nundy, K., Dev, S., Cardiff, B., John, D.: ANNet: a lightweight neural network for ECG anomaly detection in IoT edge sensors. IEEE Trans. Biomed. Circ. Syst. 16(1), 24\u201335 (2022)","journal-title":"IEEE Trans. Biomed. Circ. Syst."},{"key":"10_CR2","doi-asserted-by":"publisher","first-page":"2687","DOI":"10.2174\/0929867324666170417100318","volume":"24","author":"D Pastore","year":"2017","unstructured":"Pastore, D., Pacifici, F., Capuani, B., et al.: Sex-genetic interaction in the risk for cerebrovascular disease. Curr. Med. Chem. 24, 2687\u20132699 (2017)","journal-title":"Curr. Med. Chem."},{"key":"10_CR3","unstructured":"The top 10 causes of death. https:\/\/www.who.int\/news-room\/factsheets\/detail\/the-top-10-causes-of-death. Accessed 22 June 2023"},{"issue":"2","key":"10_CR4","first-page":"64","volume":"19","author":"HC Koh","year":"2011","unstructured":"Koh, H.C., Tan, G.: Data mining applications in healthcare. J. Healthc. Inf. Manag. 19(2), 64\u201372 (2011)","journal-title":"J. Healthc. Inf. Manag."},{"issue":"4","key":"10_CR5","doi-asserted-by":"publisher","first-page":"2431","DOI":"10.1007\/s10916-011-9710-5","volume":"36","author":"I Yoo","year":"2012","unstructured":"Yoo, I., et al.: Data mining in healthcare and biomedicine: a survey of the literature. J. Med. Syst. 36(4), 2431\u20132448 (2012). https:\/\/doi.org\/10.1007\/s10916-011-9710-5","journal-title":"J. Med. Syst."},{"issue":"12","key":"10_CR6","doi-asserted-by":"publisher","first-page":"3754","DOI":"10.1161\/STR.0000000000000046","volume":"45","author":"JF Meschia","year":"2014","unstructured":"Meschia, J.F., et al.: Guidelines for the primary prevention of stroke: a statement for healthcare professionals from the American Heart Association\/American Stroke Association. Stroke 45(12), 3754\u20133832 (2014)","journal-title":"Stroke"},{"issue":"7","key":"10_CR7","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1161\/01.STR.0000226604.10877.fc","volume":"37","author":"P Harmsen","year":"2006","unstructured":"Harmsen, P., Lappas, G., Rosengren, A., Wilhelmsen, L.: Long-term risk factors for stroke: twenty-eight years of follow-up of 7457 middle-aged men in Goteborg, Sweden. Stroke 37(7), 1663\u20131667 (2006)","journal-title":"Stroke"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Nwosu, C.S., Dev, S., Bhardwaj, P., Veeravalli, B., John, D.: Predicting stroke from electronic health records. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, pp. 5704\u20135707. IEEE (2019)","DOI":"10.1109\/EMBC.2019.8857234"},{"key":"10_CR9","doi-asserted-by":"publisher","first-page":"210318","DOI":"10.1109\/ACCESS.2020.3039439","volume":"8","author":"MS Pathan","year":"2020","unstructured":"Pathan, M.S., Jianbiao, Z., John, D., Nag, A., Dev, S.: Identifying stroke indicators using rough sets. IEEE Access 8, 210318\u2013210327 (2020)","journal-title":"IEEE Access"},{"key":"10_CR10","first-page":"473","volume":"28","author":"J Kim","year":"1995","unstructured":"Kim, J., Hong, D., Park, S.: A case-control study of risk factors for cerebrovascular disease. J. Prev. Med. 28, 473\u2013486 (1995)","journal-title":"J. Prev. Med."},{"key":"10_CR11","first-page":"27","volume":"31","author":"JK Park","year":"1998","unstructured":"Park, J.K., Kang, M.G., Kim, C.-B., et al.: A meta-analysis on the risk factors of cerebrovascular disorders in Koreans. J. Prev. Med. Public Health 31, 27\u201348 (1998)","journal-title":"J. Prev. Med. Public Health"},{"issue":"4","key":"10_CR12","doi-asserted-by":"publisher","first-page":"378","DOI":"10.5114\/fn.2021.112007","volume":"59","author":"Y Shi","year":"2021","unstructured":"Shi, Y., et al.: Risk factors for ischemic stroke: differences between cerebral small vessel and large artery atherosclerosis aetiologies. Folia Neuropathol. 59(4), 378\u2013385 (2021)","journal-title":"Folia Neuropathol."},{"key":"10_CR13","first-page":"4753","volume":"1","author":"SM Hanifa","year":"2010","unstructured":"Hanifa, S.M., Raja-S, K.: Stroke risk prediction through nonlinear support vector classification models. Int. J. Adv. Res. Comput. Sci. 1, 4753 (2010)","journal-title":"Int. J. Adv. Res. Comput. Sci."},{"key":"10_CR14","doi-asserted-by":"publisher","first-page":"180","DOI":"10.3389\/fneur.2017.00180","volume":"8","author":"BB Clissold","year":"2017","unstructured":"Clissold, B.B., Sundararajan, V., Cameron, P., et al.: Stroke incidence in Victoria, Australia\u2014emerging improvements. Front. Neurol. 8, 180 (2017)","journal-title":"Front. Neurol."},{"key":"10_CR15","doi-asserted-by":"publisher","first-page":"670379","DOI":"10.3389\/fneur.2021.670379","volume":"12","author":"S Rana","year":"2021","unstructured":"Rana, S., et al.: Application of machine learning techniques to identify data reliability and factors affecting outcome after stroke using electronic administrative records. Front. Neurol. 12, 670379 (2021)","journal-title":"Front. Neurol."},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Khosla, A., Cao, Y., Lin, C.C.Y., Chiu, H.K., Hu, J., Lee, H.: An integrated machine learning approach to stroke prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 183\u2013192 (2010)","DOI":"10.1145\/1835804.1835830"},{"key":"10_CR17","doi-asserted-by":"publisher","first-page":"e0213007","DOI":"10.1371\/journal.pone.0213007","volume":"14","author":"CY Hung","year":"2019","unstructured":"Hung, C.Y., Lin, C.H., Lan, T.H., Peng, G.S., Lee, C.C.: Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database. PLoS ONE 14, e0213007 (2019)","journal-title":"PLoS ONE"},{"issue":"1","key":"10_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-018-0702-y","volume":"18","author":"D Teoh","year":"2018","unstructured":"Teoh, D.: Towards stroke prediction using electronic health records. BMC Med. Inform. Decis. Making 18(1), 1\u201311 (2018)","journal-title":"BMC Med. Inform. Decis. Making"},{"key":"10_CR19","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":"10_CR20","unstructured":"Fed Soriano, Stroke Prediction Dataset. https:\/\/www.kaggle.com\/datasets\/fedesoriano\/stroke-prediction-datase. Accessed 23 June 2023"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Innovations and Interdisciplinary Solutions for Underserved Areas"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-51849-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T05:18:36Z","timestamp":1706764716000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-51849-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031518485","9783031518492"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-51849-2_10","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"InterSol","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Innovations and Interdisciplinary Solutions for Underserved Areas","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Flic en Flac","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mauritius","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":"16 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"intersol2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/interdisciplinarysolutions.eai-conferences.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"83","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"34","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}