COMPARISON OF MACHINE LEARNING TECHNIQUES FOR INDIRECT ASSESSMENT METHODS OF BODY CORE TEMPERATURE

Abstract

Pandemic conditions are once again in great prominence with the recent situation caused by COVID-19, some of these conditions present feverish states that can be de tected by means of mass screening at places of great influx of people. There are available different indirect methods to estimate human body core temperature. Being a febrile state considered of a body core temperature higher than 37.5 ºC. This value may differ according to the indirect method used, which can make it difficult to identify febrile cases close to the threshold value, for assisting in this task advanced Artificial Intelligence tools such as Machine Learning (ML) algorithms may be an important aid. The aim of this research is to evaluate which ML technique has the best performance with a certain indirect method of as sessing body temperature, considering the reference pro vided by another method.