A novel software engineering approach toward using machine learning for improving the efficiency of health systems

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dc.contributor.authorMoreb, Mohammed
dc.contributor.authorMohammed, Tareq Abed
dc.contributor.authorBayat, Oğuz
dc.date.accessioned2021-05-15T11:34:03Z
dc.date.available2021-05-15T11:34:03Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionMoreb, Dr. Mohammed/0000-0001-8146-6918
dc.description.abstractRecently, machine learning has become a hot research topic. Therefore, this study investigates the interaction between software engineering and machine learning within the context of health systems. We proposed a novel framework for health informatics: the framework and methodology of software engineering for machine learning in health informatics (SEMLHI). The SEMLHI framework includes four modules (software, machine learning, machine learning algorithms, and health informatics data) that organize the tasks in the framework using a SEMLHI methodology, thereby enabling researchers and developers to analyze health informatics software from an engineering perspective and providing developers with a new road map for designing health applications with system functions and software implementations. Our novel approach sheds light on its features and allows users to study and analyze the user requirements and determine both the function of objects related to the system and the machine learning algorithms that must be applied to the dataset. Our dataset used in this research consists of real data and was originally collected from a hospital run by the Palestine government covering the last three years. The SEMLHI methodology includes seven phases: designing, implementing, maintaining and defining workflows; structuring information; ensuring security and privacy; performance testing and evaluation; and releasing the software applications.en_US
dc.identifier.doi10.1109/ACCESS.2020.2970178
dc.identifier.endpage23178en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85081084578
dc.identifier.scopusqualityQ1
dc.identifier.startpage23169en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.2970178
dc.identifier.urihttps://hdl.handle.net/20.500.12939/271
dc.identifier.volume8en_US
dc.identifier.wosWOS:000525397700007
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBayat, Oğuz
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHealth Dataset Analysisen_US
dc.subjectMachine Learningen_US
dc.subjectMethodologyen_US
dc.subjectSoftware Development Managementen_US
dc.subjectSoftware Engineeringen_US
dc.titleA novel software engineering approach toward using machine learning for improving the efficiency of health systems
dc.typeArticle

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