{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,14]],"date-time":"2025-12-14T16:09:28Z","timestamp":1765728568299,"version":"build-2065373602"},"reference-count":66,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2015,7,3]],"date-time":"2015-07-03T00:00:00Z","timestamp":1435881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Diabetes is a chronic disease characterized by high blood glucose level that results either from a deficiency of insulin produced by the body, or the body\u2019s resistance to the effects of insulin. Accurate and precise reasoning and prediction models greatly help physicians to improve diagnosis, prognosis and treatment procedures of different diseases. Though numerous models have been proposed to solve issues of diagnosis and management of diabetes, they have the following drawbacks: (1) restricted one type of diabetes; (2) lack understandability and explanatory power of the techniques and decision; (3) limited either to prediction purpose or management over the structured contents; and (4) lack competence for dimensionality and vagueness of patient\u2019s data. To overcome these issues, this paper proposes a novel hybrid rough set reasoning model (H2RM) that resolves problems of inaccurate prediction and management of type-1 diabetes mellitus (T1DM) and type-2 diabetes mellitus (T2DM). For verification of the proposed model, experimental data from fifty patients, acquired from a local hospital in semi-structured format, is used. First, the data is transformed into structured format and then used for mining prediction rules. Rough set theory (RST) based techniques and algorithms are used to mine the prediction rules. During the online execution phase of the model, these rules are used to predict T1DM and T2DM for new patients. Furthermore, the proposed model assists physicians to manage diabetes using knowledge extracted from online diabetes guidelines. Correlation-based trend analysis techniques are used to manage diabetic observations. Experimental results demonstrate  that the proposed model outperforms the existing methods with 95.9% average and  balanced accuracies.<\/jats:p>","DOI":"10.3390\/s150715921","type":"journal-article","created":{"date-parts":[[2015,7,3]],"date-time":"2015-07-03T16:19:37Z","timestamp":1435940377000},"page":"15921-15951","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus"],"prefix":"10.3390","volume":"15","author":[{"given":"Rahman","family":"Ali","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3862-8787","authenticated-orcid":false,"given":"Jamil","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Siddiqi","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maqbool","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sungyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.2337\/diacare.24.11.1936","article-title":"Projection of diabetes burden through 2050","volume":"24","author":"Boye","year":"2001","journal-title":"Diabetes Care"},{"key":"ref_2","first-page":"606","article-title":"Type 2 diabetes risk forecasting from emr data using machine learning","volume":"2012","author":"Mani","year":"2012","journal-title":"AMIA Annu. 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