{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T20:19:44Z","timestamp":1742933984666,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031064579"},{"type":"electronic","value":"9783031064586"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-06458-6_16","type":"book-chapter","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T15:07:58Z","timestamp":1652368078000},"page":"197-205","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Analysis of Decision Tree Algorithms for Diabetes Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1391-1052","authenticated-orcid":false,"given":"Youssef","family":"Fakir","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7616-6785","authenticated-orcid":false,"given":"Naoum","family":"Abdelmotalib","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,13]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Matuszewski, W., et al.: Prevalence of Diabetic Retinopathy in Type 1 and Type 2 Diabetes Mellitus Patients in North-East Poland. Medecina (2020)","DOI":"10.3390\/medicina56040164"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Roy, M.S., et al.: The prevalence of diabetic retinopathy among adult type 1 diabetic persons in the United States. Arch. Ophthalmol. 122 (2004). (\u00a92004 American Medical Association)","DOI":"10.1001\/archopht.122.4.546"},{"key":"16_CR3","doi-asserted-by":"publisher","unstructured":"Wang, S.Y., Andrews, C.A., Herman, W.H., Gardner, T.W., Stein, J.D.: Incidence and Risk Factors for Developing Diabetic Retinopathy among Youths with Type 1 or Type 2 Diabetes throughout the United States, American society of ophthalmology (2017) https:\/\/doi.org\/10.1016\/j.ophtha.2016.10.031","DOI":"10.1016\/j.ophtha.2016.10.031"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Fiarni, C., Sipayung, E.M., Maemunah, S.: Analysis and prediction of diabetes complication disease using data mining algorithm. In: The Fifth Information Systems International Conference 2019, Science Direct. Procedia Computer Science, vol. 161, pp. 449\u2013457 (2019)","DOI":"10.1016\/j.procs.2019.11.144"},{"key":"16_CR5","doi-asserted-by":"publisher","first-page":"100330","DOI":"10.1016\/j.imu.2020.100330","volume":"19","author":"A.K. G\u00e1rate-Escamila","year":"2020","unstructured":"G\u00e1rate-Escamila, A..K.., Hassani, A..H..E.., Andr\u00e8s, E..: Classification models for heart disease prediction using feature selection and PCA. Inf. Med. Unlock. 19, 100330 (2020). https:\/\/doi.org\/10.1016\/j.imu.2020.100330","journal-title":"Inf. Med. Unlock."},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Mujumdar, A., Vaidehi, V.: Diabetes prediction using machine learning algorithms. In: International Conference on Recent Trends in Advanced Computing 2019, ICRTAC 2019 (2019)","DOI":"10.1016\/j.procs.2020.01.047"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Ghosh, P., Azam, A., Karim, A., Hassan, M., Roy, K., Jonkman, M.: A comparative study of different machine learning tools in detecting diabetes. 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems. Procedia Comput. Sci. 192, 467\u2013477 (2021)","DOI":"10.1016\/j.procs.2021.08.048"},{"key":"16_CR8","unstructured":"Viloria, A., Herazo-Beltran, Y., Cabrera, D., Pineda, O.B.: Diabetes diagnostic prediction using vector support machines. In: The 11th International Conference on Ambient Systems, Networks and Technologies (ANT), 6\u20139 April 2020, Warsaw, Poland (2020)"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Zhang, X., Xiao, H., Gao, R., Zhang, H., Wang, Y.: K-nearest neighbors rule combining prototype selection and local feature weighting for classification. Knowl. Based Syst. 243 (2022)","DOI":"10.1016\/j.knosys.2022.108451"},{"key":"16_CR10","unstructured":"Patel, B.R., Rana, K.K.: A survey on decision tree algorithm for classification. Int. J. Eng. Dev. Res. 2(1) (2014)"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Sisodia, D., Sisdia, D.S.: Prediction of diabetes using classification algorithms. In: International Conference on Computational Intelligence and Data Sciences (ICCIDS), Science Direct Procedia Computer Science, vol. 132, pp. 1578\u20131585 (2018)","DOI":"10.1016\/j.procs.2018.05.122"},{"issue":"10","key":"16_CR12","first-page":"14","volume":"4","author":"HH Harz","year":"2020","unstructured":"Harz, H.H., Rafi, A.O., Hijazi, M.O., Abu-Naser, S.S.: Artifical neural network for diabetes using JNN. Int. J. Acad. Eng. Res. 4(10), 14\u201322 (2020)","journal-title":"Int. J. Acad. Eng. Res."},{"key":"16_CR13","doi-asserted-by":"publisher","unstructured":"Liu, J., Tang, Z.H., Zeng, F., Li, Z., Zhou, L.: Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population. BMC Med. Inf. Dec. Mak. 13(1) (2013). https:\/\/doi.org\/10.1186\/1472-6947-13-80","DOI":"10.1186\/1472-6947-13-80"},{"key":"16_CR14","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/B978-0-12-819061-6.00014-8","volume":"121","author":"N Pradhan","year":"2020","unstructured":"Pradhan, N., Rani, G., Dhaka, V.S., Poonia, R.C.: Diabetes prediction using artificial neural network. Deep Learn. Tech. Biomed. Health Inf. 121, 327\u2013339 (2020). https:\/\/doi.org\/10.1016\/B978-0-12-819061-6.00014-8","journal-title":"Deep Learn. Tech. Biomed. Health Inf."},{"key":"16_CR15","doi-asserted-by":"publisher","unstructured":"Temurtas, H., Yumusak, N., Temurtas, F.: A comparative study on diabetes disease diagnosis using neural networks. Expert Syst. Appl. 36(4), 8610\u20138615 (2009). https:\/\/doi.org\/10.1016\/j.eswa.2008.10.032","DOI":"10.1016\/j.eswa.2008.10.032"},{"key":"16_CR16","unstructured":"Sharma, A.K., Sahni, S.: A comparative study of classification algorithms for spam email data analysis. Int. J. Comput. Sci. Eng. 3(5), 1890\u20131895 (2011)"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Nemae, D.R., Gupa, R.K.: Diabetes prediction using BPSO and decision tree classifier. In: 2nd International Conference on Data, Engineering and Applications (IDEA), IEEE Xplore 2020 (2020)","DOI":"10.1109\/IDEA49133.2020.9170744"},{"key":"16_CR18","unstructured":"Nancy, P., Ramani, R.G., Jacob, S.G.: Discovery of gender classification rules for social network data using data mining algorithms. In: Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC 2011); Kanyakumari, India (2011)"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Yuvaraj, N., Chang, V., Pinagapani, A., Kannan, S., Dhiman, G., Rajan, A.R.: Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification, Elsevier. Comput. Electric. Eng. 92 (2021)","DOI":"10.1016\/j.compeleceng.2021.107186"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Kumar, B.M., Perumal, R.S., Nadesh, R.K., Arivuselvan, K.: Type 2: diabetes mellitus prediction using Deep Neural Networks classifier. Int. J. Cogn. Comput. Eng. 1, 55\u201361 (2020)","DOI":"10.1016\/j.ijcce.2020.10.002"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Strzelecka, A., Zawadzka, D.: Application of classification and regression tree (CRT) analysis to identify the agricultural households at risk of financial exclusion. Procedia Comput. Sci. 192, 4532\u20134541 (2021)","DOI":"10.1016\/j.procs.2021.09.231"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Sharma, S., Agrawal, J., Sharma, S.: Classification through Machine Learning Technique: C4.5 Algorithm based on Various Entropies No 16 (2013)","DOI":"10.5120\/14249-2444"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Domingos, P.: MetaCost: a general method for making classifiers cost-sensitive. In: Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pp. 155\u2013164. ACM Press, San Diego, CA (1999)","DOI":"10.1145\/312129.312220"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Chawla, N.V., Japkowicz, N., Kolcz, A. (eds.) Special Issue on Learning from Imbalanced Datasets. SIGKDD, vol. 6, issue 1. ACM Press (2004)","DOI":"10.1145\/3262579"},{"key":"16_CR25","unstructured":"Zubek, V.B., Dietterich, T.: Pruning improves heuristic search for cost-sensitive learning. In: Proceedings of the Nineteenth International Conference of Machine Learning, pp. 27\u201335, Morgan Kaufmann, Sydney, Australia (2002)"},{"key":"16_CR26","unstructured":"Madadipouya, K.: A new decision tree method for data mining in medicine. Adv. Comput. Intell. Int. J. 2(3) (2015)"}],"container-title":["Lecture Notes in Business Information Processing","Business Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06458-6_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:30:36Z","timestamp":1710329436000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06458-6_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031064579","9783031064586"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06458-6_16","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"type":"print","value":"1865-1348"},{"type":"electronic","value":"1865-1356"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"13 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CBI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Business Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Khouribga","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cbi2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.cbi-bm.com\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"68","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":"23","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":"34% - 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":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}