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

Facultad de Ingeniería

Universidad Peruana de Ciencias Aplicadas

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

4 Citas (Scopus)

Resumen

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).

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781509063628
DOI	https://doi.org/10.1109/INTERCON.2017.8079667
Estado	Publicada - 20 oct. 2017
Evento	24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 - Cusco, Perú Duración: 15 ago. 2017 → 18 ago. 2017

Serie de la publicación

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

Conferencia

Conferencia	24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017
País/Territorio	Perú
Ciudad	Cusco
Período	15/08/17 → 18/08/17

ODS de las Naciones Unidas

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10.1109/INTERCON.2017.8079667

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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. En Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 Artículo 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).

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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",

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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. En 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, Perú, 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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

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DO - 10.1109/INTERCON.2017.8079667

M3 - Contribución a la conferencia

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PB - Institute of Electrical and Electronics Engineers Inc.

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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. En 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

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