Big data analytics for obesity prediction

dc.contributor	Vellido Alcacena, Alfredo
dc.contributor	Ribas Ripoll, Vicent
dc.contributor.author	Bilal, Ahsan
dc.date.accessioned	2018-07-06T09:18:56Z
dc.date.available	2018-07-06T09:18:56Z
dc.date.issued	2018-04-26
dc.identifier.uri	http://hdl.handle.net/2117/119037
dc.description.abstract	Feature selection is an important technique to find the most relevant features. Apache Spark is a big data processing framework but unable to cope with approx. 0.74 million features in our Obesity dataset. However, we tackle this challenge in 2-phase pipeline.
dc.language.iso	eng
dc.publisher	Universitat Politècnica de Catalunya
dc.subject	Àrees temàtiques de la UPC::Informàtica
dc.subject.lcsh	Big data
dc.subject.lcsh	Machine learning
dc.subject.lcsh	Genomics
dc.subject.other	Obesity Prediction
dc.subject.other	Feature Selection
dc.subject.other	Apache Spark
dc.subject.other	Genomic Data
dc.subject.other	SNPs
dc.title	Big data analytics for obesity prediction
dc.title.alternative	Big data analytics in healt
dc.type	Master thesis
dc.subject.lemac	Dades massives
dc.subject.lemac	Aprenentatge automàtic
dc.subject.lemac	Genòmica
dc.identifier.slug	131677
dc.rights.access	Open Access
dc.date.updated	2018-04-30T04:01:34Z
dc.audience.educationlevel	Màster
dc.audience.mediator	Facultat d'Informàtica de Barcelona
dc.audience.degree	MÀSTER UNIVERSITARI EN INNOVACIÓ I RECERCA EN INFORMÀTICA (Pla 2012)
dc.contributor.covenantee	Fundació Eurecat

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