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Publication
COMPARISON OF MACHINE LEARNING TECHNIQUES FOR INDIRECT ASSESSMENT METHODS OF BODY CORE TEMPERATURE
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Advisor(s)
Abstract(s)
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.