Predictive modeling for presumptive diagnosis of type 2 diabetes mellitus based on symptomatic analysis

2.50
Hdl Handle:
http://hdl.handle.net/10757/622534
Title:
Predictive modeling for presumptive diagnosis of type 2 diabetes mellitus based on symptomatic analysis
Authors:
Barrios, Ordonez; Alberto, Diego; Infantes, Vizcarra; Raphael, Erick; Aguirre, Armas; Alexander, Jimmy
Publisher:
Institute of Electrical and Electronics Engineers Inc.
Issue Date:
Aug-2017
URI:
http://hdl.handle.net/10757/622534
DOI:
10.1109/INTERCON.2017.8079667
Additional Links:
http://ieeexplore.ieee.org/document/8079667/
Abstract:
The purpose of using Predictive Modeling for presumptive diagnosis of Type 2 Diabetes Mellitus based on symptomatic analysis is the optimization of the diagnosis phase of the disease through the process of evaluating symptomatic characteristics and daily habits, allowing the forecasting of T2DM without the need of medical exams through predictive analysis. The tool used was SAP Predictive Analytics and in order to identify the most suitable algorithm for the prediction, we evaluated them based on precision and false positive/negative relations, having found the Auto Classification algorithm as the most accurate with a 91.7% precision and a better correlation between false positives (8) and false negatives (3).
Type:
info:eu-repo/semantics/conferenceObject
Rights:
info:eu-repo/semantics/rectrictedAccess
Language:
eng
Description:
El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
Keywords:
Auto Classification algorithm; Diabetes Mellitus; Predictive analytics; Diagnosis; Optimization; Predictive analytics; Classification algorithm; False negatives

Full metadata record

DC FieldValue Language
dc.contributor.authorBarrios, Ordonezes
dc.contributor.authorAlberto, Diegoes
dc.contributor.authorInfantes, Vizcarraes
dc.contributor.authorRaphael, Erickes
dc.contributor.authorAguirre, Armases
dc.contributor.authorAlexander, Jimmyes
dc.date.accessioned2018-01-16T19:56:46Z-
dc.date.available2018-01-16T19:56:46Z-
dc.date.issued2017-08-
dc.identifier.doi10.1109/INTERCON.2017.8079667-
dc.identifier.urihttp://hdl.handle.net/10757/622534-
dc.descriptionEl texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.es_PE
dc.description.abstractThe purpose of using Predictive Modeling for presumptive diagnosis of Type 2 Diabetes Mellitus based on symptomatic analysis is the optimization of the diagnosis phase of the disease through the process of evaluating symptomatic characteristics and daily habits, allowing the forecasting of T2DM without the need of medical exams through predictive analysis. The tool used was SAP Predictive Analytics and in order to identify the most suitable algorithm for the prediction, we evaluated them based on precision and false positive/negative relations, having found the Auto Classification algorithm as the most accurate with a 91.7% precision and a better correlation between false positives (8) and false negatives (3).es
dc.formatapplication/pdfes
dc.language.isoenges
dc.publisherInstitute of Electrical and Electronics Engineers Inc.es
dc.relation.urlhttp://ieeexplore.ieee.org/document/8079667/es
dc.rightsinfo:eu-repo/semantics/rectrictedAccesses
dc.subjectAuto Classification algorithmes
dc.subjectDiabetes Mellituses
dc.subjectPredictive analyticses
dc.subjectDiagnosises
dc.subjectOptimizationes
dc.subjectPredictive analyticses
dc.subjectClassification algorithmes
dc.subjectFalse negativeses
dc.titlePredictive modeling for presumptive diagnosis of type 2 diabetes mellitus based on symptomatic analysises
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dc.description.peerreviewRevisión por pareses_PE
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