Performance analysis of disease diagnostic system using IoMT and real-time data analytics

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dc.authoridÇalhan, Ali/0000-0002-5798-3103
dc.authoridCİCİOĞLU, MURTAZA/0000-0002-5657-7402
dc.authoridYıldırım, Emre/0000-0002-9072-9780
dc.authorwosidÇalhan, Ali/H-1375-2014
dc.authorwosidCİCİOĞLU, MURTAZA/AAL-5004-2020
dc.contributor.authorYıldırım, Emre
dc.contributor.authorÇalhan, Ali
dc.contributor.authorCicioğlu, Murtaza
dc.date.accessioned2023-07-26T11:55:09Z
dc.date.available2023-07-26T11:55:09Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractIn this article, the Internet of Medical Things (IoMT) framework based on Apache Spark big data processing technology is proposed for real-time analysis of health data obtained from wireless body area networks (WBANs), which is one of the most important components of IoMT. The proposed framework consists of four layers: data source, data collection, data analytics and visualization. In addition, the proposed IoMT framework is presented with two different disease prediction scenarios, diabetes and heart disease. Diabetes and heart disease prediction processes are carried out using the random forest (RF), logistic regression (LR) and support vector machine (SVM) algorithms belonging to the Apache Spark machine learning library (MLlib). The analysis of health data generated in WBANs takes place in real-time in the Apache Spark-based data analytics layer. In this study, the performances of MLlib algorithms in the real-time model developed for heart and diabetes disease are examined. The SVM algorithm with an accuracy rate of 93.33% for heart disease and the LR algorithm with an accuracy rate of 78.89% for diabetes are found to provide the best performances.en_US
dc.identifier.doi10.1002/cpe.6916
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.issue13en_US
dc.identifier.scopus2-s2.0-85126002913en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1002/cpe.6916
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13008
dc.identifier.volume34en_US
dc.identifier.wosWOS:000766946700001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÇalhan, Ali
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofConcurrency and Computation-Practice & Experienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectApache Spark; Iomt; Wbans; Data Analytics; Machine Learningen_US
dc.subjectMonitoring-Systemen_US
dc.titlePerformance analysis of disease diagnostic system using IoMT and real-time data analyticsen_US
dc.typeArticleen_US

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