Supervised Learning Techniques for the Optimization of Diagnosis Processes of Diabetes in Public Health Centers

Authors

Chavez-Cujilan, Yelena

Patiño-Perez, Darwin

García-Gutierrez, Carlos

García-Gutierrez, Angel

Botto-Tobar, Miguel

Munive-Mora, Celia

Icaza-Rivera, Dalva

Abstract

One of the problems in public health care services in developing countries such as Ecuador, is the delay in the diagnosis of one of the main diseases that afflict the population such as diabetes, among public policies in the sector of health in this country is the optimization of diagnostic processes without using resources that affect the state budget in the short or long term. Through supervised learning techniques within the field of artificial intelligence, models can be created that allow the optimization of the diagnosis without the intervention of specialists for the interpretation of the results of patients who show signs of diabetes.