A descriptive statistical analysis of overweight and obesity using big data

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dc.contributor.authorAli, Salam Abdulabbas Ganim
dc.contributor.authorAl-Fayyadh, Hayder Rahm Dakheel
dc.contributor.authorMohammed, Shaimaa Hadi
dc.contributor.authorAhmed, Saadaldeen Rashid
dc.date.accessioned2022-08-05T13:40:03Z
dc.date.available2022-08-05T13:40:03Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_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.en_US
dc.identifier.citationAli, S. A. G., AL-Fayyadh, H. R. D., Mohammed, S. H., Ahmed, S. R. (2022). A descriptive statistical analysis of overweight and obesity using big data. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), IEEE.en_US
dc.identifier.isbn9781665468350
dc.identifier.scopus2-s2.0-85133972573
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2771
dc.indekslendigikaynakScopus
dc.institutionauthorAhmed, Saadaldeen Rashid
dc.language.isoen
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, Proceedings
dc.relation.isversionof10.1109/HORA55278.2022.9800098en_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - İdari Personel ve Öğrencien_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 data
dc.typeConference Object

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