{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:06:38Z","timestamp":1750309598005,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":16,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,12,2]]},"DOI":"10.1145\/3708635.3708653","type":"proceedings-article","created":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T12:57:34Z","timestamp":1745672254000},"page":"119-124","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparing the Performance of a CDC Questionnaire with Machine Learning Models in Predicting Diabetes"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0169-3272","authenticated-orcid":false,"given":"Yusuf","family":"Alatawi","sequence":"first","affiliation":[{"name":"College of Engineering & Computing, American University of Bahrain, Riffa, Bahrain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7995-1947","authenticated-orcid":false,"given":"Hasan","family":"Kadhem","sequence":"additional","affiliation":[{"name":"College of Engineering & Computing, American University of Bahrain, Riffa, Bahrain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,4,26]]},"reference":[{"key":"e_1_3_3_1_2_2","unstructured":"Ann Pietrangelo. 2016. Types of Diabetes: Causes Identification and More. https:\/\/www.healthline.com\/health\/diabetes\/types-of-diabetes"},{"key":"e_1_3_3_1_3_2","unstructured":"Centers for Disease Control and Prevention. 2021. Prediabetes - Your Chance to Prevent Type 2 Diabetes. https:\/\/www.cdc.gov\/diabetes\/basics\/prediabetes.html"},{"key":"e_1_3_3_1_4_2","unstructured":"Centers for Disease Control and Prevention. 2022. Prediabetes Risk Test. https:\/\/www.cdc.gov\/prediabetes\/risktest\/index.html"},{"key":"e_1_3_3_1_5_2","unstructured":"Centers for Disease Control and Prevention. 2023. Diabetes Testing. https:\/\/www.cdc.gov\/diabetes\/basics\/getting-tested.html"},{"key":"e_1_3_3_1_6_2","unstructured":"Centers for Disease Control and Prevention. 2023. NHANES - National Health and Nutrition Examination Survey Homepage. https:\/\/www.cdc.gov\/nchs\/nhanes\/index.htm"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"publisher","unstructured":"Kushan De\u00a0Silva Siew Lim Aya Mousa Helena Teede Andrew Forbes Ryan\u00a0T. Demmer Daniel J\u00f6nsson and Joanne Enticott. 2021. Nutritional markers of undiagnosed type 2 diabetes in adults: Findings of a machine learning analysis with external validation and benchmarking. PloS One 16 5 (2021) e0250832. 10.1371\/journal.pone.0250832","DOI":"10.1371\/journal.pone.0250832"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/DeSE54285.2021.9719398"},{"key":"e_1_3_3_1_9_2","unstructured":"Helmholtz Munich. [n. d.]. KORA. https:\/\/www.helmholtz-munich.de\/en\/epi\/cohort\/kora"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","unstructured":"Jia-Lien Hsu Ping-Cheng Hung Hung-Yen Lin and Chung-Ho Hsieh. 2015. Applying Under-Sampling Techniques and Cost-Sensitive Learning Methods on Risk Assessment of Breast Cancer. Journal of Medical Systems 39 4 (Feb. 2015) 40. 10.1007\/s10916-015-0210-x","DOI":"10.1007\/s10916-015-0210-x"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","unstructured":"Andreas Katsimpris Aboulmaouahib Brahim Wolfgang Rathmann Anette Peters Konstantin Strauch and Ant\u00f2nia Flaquer. 2021. Prediction of type 2 diabetes mellitus based on nutrition data. Journal of Nutritional Science 10 (2021) e46. 10.1017\/jns.2021.36","DOI":"10.1017\/jns.2021.36"},{"key":"e_1_3_3_1_12_2","volume-title":"Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies (second edition ed.)","author":"Kelleher John\u00a0D.","year":"2020","unstructured":"John\u00a0D. Kelleher, Brian Mac\u00a0Namee, and Aoife D\u2019Arcy. 2020. Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies (second edition ed.). The MIT Press, Cambridge, Massachusetts."},{"key":"e_1_3_3_1_13_2","unstructured":"Mayo Clinic. [n. d.]. Diabetes - Symptoms and causes. https:\/\/www.mayoclinic.org\/diseases-conditions\/diabetes\/symptoms-causes\/syc-20371444"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCTCT.2018.8551020"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"publisher","unstructured":"Qian Wang Weijia Cao Jiawei Guo Jiadong Ren Yongqiang Cheng and Darryl\u00a0N. Davis. 2019. DMP_MI: An Effective Diabetes Mellitus Classification Algorithm on Imbalanced Data With Missing Values. IEEE Access 7 (2019) 102232\u2013102238. 10.1109\/ACCESS.2019.2929866Conference Name: IEEE Access.","DOI":"10.1109\/ACCESS.2019.2929866"},{"key":"e_1_3_3_1_16_2","unstructured":"World Health Organization. [n. d.]. Diabetes. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/diabetes"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"publisher","unstructured":"Quan Zou Kaiyang Qu Yamei Luo Dehui Yin Ying Ju and Hua Tang. 2018. Predicting Diabetes Mellitus With Machine Learning Techniques. Frontiers in Genetics 9 (Nov. 2018) 515. 10.3389\/fgene.2018.00515","DOI":"10.3389\/fgene.2018.00515"}],"event":{"name":"ICSIE 2024: 2024 13th International Conference on Software and Information Engineering (ICSIE)","acronym":"ICSIE 2024","location":"Derby United Kingdom"},"container-title":["Proceedings of the 2024 13th International Conference on Software and Information Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3708635.3708653","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3708635.3708653","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:57:03Z","timestamp":1750298223000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3708635.3708653"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"references-count":16,"alternative-id":["10.1145\/3708635.3708653","10.1145\/3708635"],"URL":"https:\/\/doi.org\/10.1145\/3708635.3708653","relation":{},"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"2025-04-26","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}