Chagas’ disease affects about 10 million people
in Latin America. It is mainly transmitted by the faeces of
triatomine bugs. The illness in humans can be recognized with
a conventional Machado-Guerreiro test. However, it evolves in
different stages. To determine the miocardial damage inflicted
by the disease in the chronic stage it is necessary to perform
several expensive, time consuming and sometimes even invasive
tests.
In this article, a machine learning system (Support Vector
Machines) is trained to determine the degree of damage using
exclusively the cardiac signals obtained from High Resolution
Electrocardiogram (HRECG).
The final classifiers consist of two simple formulas whose
implementation can be easily carried out without the need of
any knowledge in computer sciences.
The research provides three significant contributions in the
subject. First, it attain high classification rates. Second, it
provides the final solution in two simple equations. Finally, it
implements an exhaustive method useful to determine the best
set of QRS features to train the classifiers.