{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:50:42Z","timestamp":1787017842606,"version":"build-2736575974"},"reference-count":35,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Healthcare Engineering"],"published-print":{"date-parts":[[2022,1,11]]},"abstract":"<jats:p>Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population\u2019s health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study\u2019s primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.<\/jats:p>","DOI":"10.1155\/2022\/1684017","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T13:22:40Z","timestamp":1641907360000},"page":"1-10","source":"Crossref","is-referenced-by-count":195,"title":["A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques"],"prefix":"10.1155","volume":"2022","author":[{"given":"Raja","family":"Krishnamoorthi","sequence":"first","affiliation":[{"name":"Department of ECE, Vignan\u2019s Institute of Management and Technology for Women, Kondapur (V), Ghatkesar (M), Medchal-Malkajgiri (D), Padamatisaiguda, Telangana 501301, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3815-1261","authenticated-orcid":true,"given":"Shubham","family":"Joshi","sequence":"additional","affiliation":[{"name":"Computer Engineering, SVKM\u2019S NMIMS, MPSTME Shirpur Campus, Savalade, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4178-1320","authenticated-orcid":true,"given":"Hatim Z.","family":"Almarzouki","sequence":"additional","affiliation":[{"name":"Department of Radiology, Faculty of Medicine, King Abdulaziz University Hospital, Jeddah, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3715-3882","authenticated-orcid":true,"given":"Piyush Kumar","family":"Shukla","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering Department, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (Technological University of Madhya Pradesh), Bhopal 462033, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2348-8577","authenticated-orcid":true,"given":"Ali","family":"Rizwan","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C.","family":"Kalpana","sequence":"additional","affiliation":[{"name":"Dept. of CSE SST College of Art and Commerce, Ulhasnagar, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3113-4760","authenticated-orcid":true,"given":"Basant","family":"Tiwari","sequence":"additional","affiliation":[{"name":"Ethiopia Hawassa University, Awasa, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2019.100204"},{"key":"2","article-title":"Prediction of Diabetes Using Machine Learning Algorithms in Healthcare","author":"M. A. Sarwar"},{"key":"3","doi-asserted-by":"crossref","DOI":"10.1007\/s42452-019-1759-7","article-title":"A Robust Voting Approach for Diabetes Prediction Using Traditional Machine Learning Techniques","volume-title":"SN Applied Sciences","author":"A. Mahabub","year":"2019"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5525271"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1007\/s13755-019-0095-z"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/j.livsci.2015.06.019"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2012.04.013"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.31873\/ijetr.9.6.2019.64"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2006.09.005"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_2"},{"issue":"3","key":"11","doi-asserted-by":"crossref","first-page":"124","DOI":"10.2478\/cait-2013-0030","article-title":"Design of a diabetic diagnosis system using rough sets","volume":"13","author":"M. Anouncia","year":"2013","journal-title":"Cybernetics and Information Technologies"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1016\/j.biortech.2014.04.013"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.04.036"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.05.018"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2011.03.018"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2018.00515"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3041355"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2989857"},{"key":"19","article-title":"Optimizing hyper parameters for im- proved diabetes prediction","volume":"5","author":"S. Kapoor","year":"2018","journal-title":"International Research Journal of Engineering and Technology"},{"key":"20","article-title":"Prediction of diabetes using artificial neural network approach","volume-title":"Engineering Vibration, Communication and Information Processing","author":"S. Srivastava","year":"2020"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.03.185"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.10.014"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.01.047"},{"issue":"1","key":"24","doi-asserted-by":"crossref","first-page":"19","DOI":"10.4018\/JOEUC.2021010102","article-title":"Taxonomy on EEG artifacts removal methods, issues, and healthcare applications","volume":"33","author":"V. Roy","year":"2021","journal-title":"Journal of Organizational and End User Computing"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2021.07.262"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/7804540"},{"key":"27","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s40009-020-00998-w","article-title":"Image forgery detection using singular value decomposition with some attacks","volume":"44","author":"N. K. Rathore","year":"2021","journal-title":"National Academy Science Letters"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.35940\/ijrteB3048.078219"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/9674712"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.5815\/IJCNIS.2015.12.08"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8715668"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2019.09.007"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1038\/nature12881"},{"key":"34","volume-title":"National Institute of Diabetes, Digestive, & Kidney Diseases (Us)","author":"National Heart Lung Blood Institute","year":"1995"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1016\/0009-8981(79)90364-4"}],"updated-by":[{"DOI":"10.1155\/2023\/9872970","type":"retraction","label":"Retraction","source":"retraction-watch","updated":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T00:00:00Z","timestamp":1684886400000},"record-id":"45426"},{"DOI":"10.1155\/2023\/9872970","type":"retraction","label":"Retraction","source":"publisher","updated":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T00:00:00Z","timestamp":1684886400000}}],"container-title":["Journal of Healthcare Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/jhe\/2022\/1684017.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jhe\/2022\/1684017.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jhe\/2022\/1684017.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T13:22:55Z","timestamp":1641907375000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/jhe\/2022\/1684017\/"}},"subtitle":[],"editor":[{"given":"Deepak","family":"Garg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2022,1,11]]},"references-count":35,"alternative-id":["1684017","1684017"],"URL":"https:\/\/doi.org\/10.1155\/2022\/1684017","relation":{},"ISSN":["2040-2309","2040-2295"],"issn-type":[{"value":"2040-2309","type":"electronic"},{"value":"2040-2295","type":"print"}],"subject":[],"published":{"date-parts":[[2022,1,11]]}}}