A Descriptive Statistical Analysis of Overweight and Obesity Using Big Data

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dc.contributor.authorAli, S.A.G.
dc.contributor.authorAl-Fayyadh, H.R.D.
dc.contributor.authorMohammed, S.H.
dc.contributor.authorAhmed, S.R.
dc.date.accessioned2024-09-29T16:20:55Z
dc.date.available2024-09-29T16:20:55Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2022 -- 9 June 2022 through 11 June 2022 -- Ankara -- 180434en_US
dc.description.abstractIn this paper, we have obtained the dataset from an open-source repository for obese people by focused on a descriptive statistical analysis of overweight and obesity using big data. We performed the statistical analysis on large scale streaming data for obesity prediction. We have classified the obesity with all categories on the scale of Body Mass Index (BMI) is being calculated i.e., underweight, normal weight, overweight, obese, very obese, and extremely obese using MapReduce technique with the help of Apache Spark and Apache Hadoop engine in pydoop python programming. The MapReduce technique in-volves the updating of cluster centers after arrival of new batch in the stream of data. The streaming of data is produced by the sensors which are classified into six different BMI categories, which are stored and processed through big data tools connected to the statistical analysis system. The Apache spark produces the latency values in accessing the data from dataset. We analyzed any obesity in the people from the normal latency value using the Apache spark and Hadoop which are well known in big data. The methods and techniques by which we can predict obesity efficiently from the large-scale streaming data has been per-formed using python programming. This is applied with the help of Apache Spark and Hadoop. In order to validate the efficiency of MapReduce technique. We have tested it both on single and distributed environment for obesity prediction using the built-in Pydoop package in python. © 2022 IEEE.en_US
dc.identifier.doi10.1109/HORA55278.2022.9800098
dc.identifier.isbn978-166546835-0
dc.identifier.scopus2-s2.0-85133972573en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/HORA55278.2022.9800098
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9420
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofHORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectApacheen_US
dc.subjectBig dataen_US
dc.subjectprocess managementen_US
dc.subjectPydoopen_US
dc.subjectRisk Managementen_US
dc.titleA Descriptive Statistical Analysis of Overweight and Obesity Using Big Dataen_US
dc.typeConference Objecten_US

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