  Patients suffering from pulmonary diseases typically exhibit pathological
lung ventilation in terms of homogeneity. Electrical Impedance Tomography (EIT)
is a non-invasive imaging method that allows to analyze and quantify the
distribution of ventilation in the lungs. In this article, we present a new
approach to promote the use of EIT data and the implementation of new clinical
applications for differential diagnosis, with the development of several
machine learning models to discriminate between EIT data from healthy and
non-healthy subjects. EIT data from 16 subjects were acquired: 5 healthy and 11
non-healthy subjects (with multiple pulmonary conditions). Preliminary results
have shown accuracy percentages of 66\% in challenging evaluation scenarios.
The results suggest that the pairing of EIT feature engineering methods with
machine learning methods could be further explored and applied in the
diagnostic and monitoring of patients suffering from lung diseases. Also, we
introduce the use of a new feature in the context of EIT data analysis
(Impedance Curve Correlation).
