Data Science in the Field of Health

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dc.contributor.authorKulan, H.
dc.contributor.authorÖzer, E.
dc.date.accessioned2024-05-19T14:33:47Z
dc.date.available2024-05-19T14:33:47Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractData science in healthcare has made great progress using data analysis and machine learning methods that have the potential to detect and help solve healthcare problems. After mortality and during morbidity, relevant data about a health problem have been gathered. This massive amount of data in various forms needs to be handled for any healthcare issues are significant. With the growth of big data in healthcare communities, accurate analysis of medical data has the benefits of early disease detection, improved patient care, and effective community services. Because of its significance, there is a need to develop efficient and better-performing data analytics techniques and tools to analyze medical big data from the gene level to the clinical level. The purpose of data analytics in healthcare is to find new insights in data, at least partially automate tasks such as diagnosing, and to facilitate clinical decision-making. Also, healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of disease, avoid preventable diseases, and improve the quality of life in general. The average human lifespan is increasing across the world population and the application of big data analytics in healthcare are increasing day by day. Different format of health data are used for different types of analyses. For example, IoT gadgets are used by certain patients and clinicians as wearable monitors to track heartbeat and temperature. This signal generated data should be carefully analyzed over time. Also, scans such as X-rays, magnetic resonance images (MRIs), and computed tomography (CAT) scans can be studied with different data analysis techniques and machine learning algorithms to visualize the insides of the body in depth. In addition, data on individual cases of disease are analyzed; data received as text must be sorted, categorized, and coded for statistical analysis; and data from surveys might need to be weighted to produce valid estimates for sampled populations. The number of resources healthcare professionals can obtain from their patients continues to increase. Since these data are normally in different formats and sizes, it can be difficult for analysis. However, the current focus is no longer on how big the data is, but how intelligently it is managed by data analysis techniques and machine learning algorithms. This study examines analytical techniques for different forms of health-related data to generate comprehensive healthcare reports and transform them into relevant critical insights that can then be used to provide better care. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.en_US
dc.identifier.doi10.1007/978-3-031-46735-6_3
dc.identifier.endpage28en_US
dc.identifier.issn2198-4182
dc.identifier.scopus2-s2.0-85182496079en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage19en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-46735-6_3
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4334
dc.identifier.volume513en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofStudies in Systems, Decision and Controlen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectData Analyticsen_US
dc.subjectHealthcareen_US
dc.subjectImage Processingen_US
dc.subjectMachine Learningen_US
dc.subjectSignal Processingen_US
dc.titleData Science in the Field of Healthen_US
dc.typeBook Chapteren_US

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