{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T05:19:56Z","timestamp":1743139196038,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031396885"},{"type":"electronic","value":"9783031396892"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-39689-2_10","type":"book-chapter","created":{"date-parts":[[2023,8,20]],"date-time":"2023-08-20T23:01:49Z","timestamp":1692572509000},"page":"91-96","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Effective Feature Selection for Diabetes Prediction"],"prefix":"10.1007","author":[{"given":"In-ae","family":"Kang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soualihou","family":"Ngnamsie Njimbouom","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeong-Dong","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,21]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zhang, D.: Machine learning techniques for screening and diagnosis of diabetes: a survey. Teh. Vjesn. 26, 872\u2013880 (2019)","DOI":"10.17559\/TV-20190421122826"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Ndisang, J.F., Vannacci, A., Rastogi, S.: Insulin resistance, type 1 and type 2 diabetes, and related complications 2017. J. Diabetes Res. 2017, e1478294 (2017). [PubMed]","DOI":"10.1155\/2017\/1478294"},{"key":"10_CR3","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/978-3-030-58861-8_7","volume-title":"Modelling and Implementation of Complex Systems","author":"S Malik","year":"2021","unstructured":"Malik, S., Harous, S., El-Sayed, H.: Comparative analysis of machine learning algorithms for early prediction of diabetes mellitus in women. In: Chikhi, S., Amine, A., Chaoui, A., Saidouni, D.E., Kholladi, M.K. (eds.) MISC 2020. LNNS, vol. 156, pp. 95\u2013106. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-58861-8_7"},{"key":"10_CR4","first-page":"119","volume":"4","author":"HP Himsworth","year":"1939","unstructured":"Himsworth, H.P., Kerr, R.B.: Insulin-sensitive and insulin-insensitive types of diabetes mellitus. Clin. Sci. 4, 119\u2013152 (1939)","journal-title":"Clin. Sci."},{"key":"10_CR5","unstructured":"World Health Organization, 2020 World Health Organization. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/diabetes. Accessed 8 June 2020"},{"issue":"17","key":"10_CR6","doi-asserted-by":"publisher","first-page":"13079","DOI":"10.1007\/s00521-019-04402-7","volume":"32","author":"N Theera-Umpon","year":"2019","unstructured":"Theera-Umpon, N., Poonkasem, I., Auephanwiriyakul, S., Patikulsila, D.: Hard exudate detection in retinal fundus images using supervised learning. Neural Comput. Appl. 32(17), 13079\u201313096 (2019). https:\/\/doi.org\/10.1007\/s00521-019-04402-7","journal-title":"Neural Comput. Appl."},{"issue":"6","key":"10_CR7","first-page":"968","volume":"15","author":"S Afzali","year":"2018","unstructured":"Afzali, S., Yildiz, O.: An effective sample preparation method for diabetes prediction. Int. Arab J. Inf. Technol. 15(6), 968\u2013973 (2018)","journal-title":"Int. Arab J. Inf. Technol."},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Jaiswal, V., Negi, A., Pal, T.: A review on current advances in machine learning based diabetes prediction. Prim. Care Diabetes 15, 435\u2013443 (2021)","DOI":"10.1016\/j.pcd.2021.02.005"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Tariq, H., Rashid, M., Javed, A., Zafar, E., Alotaibi, S.S., Zia, M.Y.I.: Performance analysis of deep-neural-network-based automatic diagnosis of diabetic retinopathy. Sensors 22, 205 (2022)","DOI":"10.3390\/s22010205"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Kumar, D., et al.: Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks. IEEE Access 8, 142521\u2013142531 (2020)","DOI":"10.1109\/ACCESS.2020.3012292"},{"key":"10_CR11","unstructured":"Khaleel, F.A., Al-Bakry, A.M.:Diagnosis of diabetes using machine learning algorithms. Mater. Today: Proc. (2021)"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Saxena, R., Sharma, S.K., Gupta, M., Sampada, G.C.: A comprehensive review of various diabetic prediction models: a literature survey. J. Healthc. Eng. 2022, e8100697 (2022). [PubMed]","DOI":"10.1155\/2022\/8100697"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Chatrati, S.P., et al.: Smart home health monitoring system for predicting type 2 diabetes and hypertension. J. King Saud Univ.\u2014Comput. Inf. Sci. 34, 862\u2013870 (2020)","DOI":"10.1016\/j.jksuci.2020.01.010"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Goyal, P., Jain, S.: Prediction of type-2 diabetes using classification and ensemble method approach. In: Proceedings of the 2022 International Mobile and Embedded Technology Conference (MECON), Noida, India, pp. 658\u2013665, 10\u201311 March 2022","DOI":"10.1109\/MECON53876.2022.9752268"},{"key":"10_CR15","first-page":"9","volume":"12","author":"A Prakash","year":"2021","unstructured":"Prakash, A.: An ensemble technique for early prediction of type 2 diabetes mellitus\u2014a normalization approach. Turk. J. Comput. Math. Educ. 12, 9 (2021)","journal-title":"Turk. J. Comput. Math. Educ."},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Chang, V., et al.: Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms.Neural Comput. Appl., 1-17 (2022)","DOI":"10.1007\/s00521-022-07049-z"},{"issue":"5","key":"10_CR17","doi-asserted-by":"publisher","first-page":"5198","DOI":"10.1007\/s11227-020-03481-x","volume":"77","author":"V Jackins","year":"2020","unstructured":"Jackins, V., Vimal, S., Kaliappan, M., Lee, M.Y.: AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes. J. Supercomput. 77(5), 5198\u20135219 (2020). https:\/\/doi.org\/10.1007\/s11227-020-03481-x","journal-title":"J. Supercomput."},{"key":"10_CR18","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s40537-019-0175-6","volume":"6","author":"N Sneha","year":"2019","unstructured":"Sneha, N., Tarun, G.: Analysis of diabetes mellitus for early prediction using optimal feature selection. J. Big data 6, 3 (2019)","journal-title":"J. Big data"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Kamrul Hasan, M., Ashraful Alam, M., Das, D., Hussain, E., Hasan, M.: Diabetes prediction using ensembling of different machine learning classifiers.IEEE Access 8 (2020). Article ID: 76531","DOI":"10.1109\/ACCESS.2020.2989857"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Saxena, R., Sharma, S.K., Gupta, M., Sampada, G.C.: A novel approach for feature selection and classification of diabetes mellitus: machine learning methods. Comput. Intell. Neurosci. 2022, e3820360 (2022)","DOI":"10.1155\/2022\/3820360"},{"key":"10_CR21","unstructured":"Korea Centers for Disease Control and Prevention. https:\/\/knhanes.kdca.go.kr\/knhanes\/sub03\/sub03_02_05.do"},{"key":"10_CR22","unstructured":"Khaire, U.M., Dhanalakshmi, R.: Stability of feature selection algorithm: a review. J. King Saud Univ. Comput. Inf. Sci. (2019)"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Gao, Z., Xu, Y., Meng, F., Qi, F., Lin, Z.: Improved information gain-based feature selection for text categorization. In: Proceedings of the 2014 4th International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace Electronic Systems (VITAE), IEEE, Aalborg, Denmark, pp. 1\u20135, 11\u201314 May 2014","DOI":"10.1109\/VITAE.2014.6934421"},{"key":"10_CR24","first-page":"1","volume":"50","author":"J Li","year":"2017","unstructured":"Li, J., et al.: Feature selection: a data perspective. ACM Comput. Surv. 50, 1\u201345 (2017)","journal-title":"ACM Comput. Surv."}],"container-title":["Communications in Computer and Information Science","Database and Expert Systems Applications - DEXA 2023 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-39689-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:23:51Z","timestamp":1710329031000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-39689-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031396885","9783031396892"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-39689-2_10","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"21 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DEXA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database and Expert Systems Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Penang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaysia","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":"28 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dexa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dexa.org\/dexa2023","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"155","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":"49","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":"35","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":"32% - 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":"4","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"For the workshops 7 full and 3 short papers have been accepted from 20 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}