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

Ordonez Barrios; Diego Alberto; Vizcarra Infantes; Erick Raphael; Armas Aguirre; Jimmy Alexander

doi:10.1109/INTERCON.2017.8079667

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

Ordonez Barrios
, Diego Alberto
, Vizcarra Infantes
, Erick Raphael
, Armas Aguirre
, Jimmy Alexander

Universidad Peruana de Ciencias Aplicadas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

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).

Original language	English
Title of host publication	Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509063628
DOIs	https://doi.org/10.1109/INTERCON.2017.8079667
State	Published - 20 Oct 2017
Event	24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 - Cusco, Peru Duration: 15 Aug 2017 → 18 Aug 2017

Publication series

Name	Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017

Conference

Conference	24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017
Country/Territory	Peru
City	Cusco
Period	15/08/17 → 18/08/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Auto Classification algorithm
diabetes mellitus
predictive analytics

Access to Document

10.1109/INTERCON.2017.8079667

Cite this

Barrios, O., Alberto, D., Infantes, V., Raphael, E., Aguirre, A., & Alexander, J. (2017). Predictive modeling for presumptive diagnosis of type 2 diabetes mellitus based on symptomatic analysis. In Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 Article 8079667 (Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INTERCON.2017.8079667

Barrios, Ordonez ; Alberto, Diego ; Infantes, Vizcarra et al. / Predictive modeling for presumptive diagnosis of type 2 diabetes mellitus based on symptomatic analysis. Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017. Institute of Electrical and Electronics Engineers Inc., 2017. (Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017).

@inproceedings{e20841e44ecd4fb6a5cdad409b04ac69,

title = "Predictive modeling for presumptive diagnosis of type 2 diabetes mellitus based on symptomatic analysis",

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).",

keywords = "Auto Classification algorithm, diabetes mellitus, predictive analytics",

author = "Ordonez Barrios and Diego Alberto and Vizcarra Infantes and Erick Raphael and Armas Aguirre and Jimmy Alexander",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 ; Conference date: 15-08-2017 Through 18-08-2017",

year = "2017",

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doi = "10.1109/INTERCON.2017.8079667",

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series = "Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017",

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booktitle = "Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017",

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Barrios, O, Alberto, D, Infantes, V, Raphael, E, Aguirre, A & Alexander, J 2017, Predictive modeling for presumptive diagnosis of type 2 diabetes mellitus based on symptomatic analysis. in Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017., 8079667, Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017, Institute of Electrical and Electronics Engineers Inc., 24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017, Cusco, Peru, 15/08/17. https://doi.org/10.1109/INTERCON.2017.8079667

Predictive modeling for presumptive diagnosis of type 2 diabetes mellitus based on symptomatic analysis. / Barrios, Ordonez; Alberto, Diego; Infantes, Vizcarra et al.
Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8079667 (Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Predictive modeling for presumptive diagnosis of type 2 diabetes mellitus based on symptomatic analysis

AU - Barrios, Ordonez

AU - Alberto, Diego

AU - Infantes, Vizcarra

AU - Raphael, Erick

AU - Aguirre, Armas

AU - Alexander, Jimmy

PY - 2017/10/20

Y1 - 2017/10/20

N2 - 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).

AB - 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).

KW - Auto Classification algorithm

KW - diabetes mellitus

KW - predictive analytics

UR - https://www.scopus.com/pages/publications/85039990712

U2 - 10.1109/INTERCON.2017.8079667

DO - 10.1109/INTERCON.2017.8079667

M3 - Contribución a la conferencia

AN - SCOPUS:85039990712

T3 - Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017

BT - Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017

Y2 - 15 August 2017 through 18 August 2017

ER -

Barrios O, Alberto D, Infantes V, Raphael E, Aguirre A, Alexander J. Predictive modeling for presumptive diagnosis of type 2 diabetes mellitus based on symptomatic analysis. In Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 8079667. (Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017). doi: 10.1109/INTERCON.2017.8079667

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

Abstract

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UN SDGs

Keywords

Access to Document

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