Comparativa de modelos multivariados basados en aprendizaje automático para la predicción del riesgo de diabetes en etapas tempranas.

Contenido principal del artículo

Zayra Ramírez Gaytán
Alen Francisco Luévano Lara
Vanessa Alcalá Ramírez

Resumen

Diabetes is one of the fastest-growing, life-threatening, chronic degenerative diseases.  According to the World Health Organization (WHO), it has affected 422 million people worldwide in 2018. Approximately 50% of all people who suffer diabetes are not diagnosed due to the asymptomatic phase which usually lasts a long time. In this work, a data set of 520 instances has been used. The data set has been analyzed with the next three algorithms:  logistic regression algorithm, decision trees and random forest. The results show that the decision tree algorithm had better performance with an AUC of 98%. Also, it was found the most common symptoms that a person with a risk of diabetes presents are polyuria, polydipsia and sudden weight loss.

Detalles del artículo

Cómo citar
Ramírez Gaytán, Z. ., Luévano Lara, A. F., & Alcalá Ramírez, V. (2021). Comparativa de modelos multivariados basados en aprendizaje automático para la predicción del riesgo de diabetes en etapas tempranas. Memorias Del Congreso Nacional De Ingeniería Biomédica, 8(1), 331–334. Recuperado a partir de https://memoriascnib.mx/index.php/memorias/article/view/902
Sección
Modelado y Simulación de Sistemas Biológicos, Bioinformática y Biología Compt.