| Título: | Predictive modeling for presumptive diagnosis of type 2 diabetes mellitus based on symptomatic analysis |
| Autores: | Barrios, Ordonez ; Alberto, Diego ; Infantes, Vizcarra ; Raphael, Erick ; Aguirre, Armas ; Alexander, Jimmy |
| Tipo de documento: | texto impreso |
| Editorial: | Institute of Electrical and Electronics Engineers Inc., 2018-01-16T19:56:46Z |
| Dimensiones: | application/pdf |
| Nota general: | info:eu-repo/semantics/rectrictedAccess |
| Idiomas: | Inglés |
| Palabras clave: | Facultad de Ingeniería , Pregrado , Conferencias y congresos , Ingeniería de Sistemas y Computación |
| Resumen: |
El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. 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). Revisión por pares |
| En línea: | 10.1109/INTERCON.2017.8079667 |
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