Rough Sets and Multilayer Perceptron in Tandem for Processing the Aleatory Uncertainty in COVID-19 Cases

It is common sense that challenging clinical cases can occur in the practice of medicine. These clinical cases can lead to undesirable situations of diagnostic uncertainty, making it important to identify these difficult cases and lead them to a discussion by experts for appropriate characterization. In remote/isolated regions, it is crucial to have a computational system that can support the health personnel in identifying these challenging cases. Events such as the COVID-19 pandemic have demonstrated the need for this type of system to assist in screening medical exams. Although the chest X-ray examination is a valuable diagnostic tool for COVID-19 cases, some conditions can be so challenging that doctors can be faced with uncertainty in diagnosis. This article proposes a system that combines machine learning via Multilayer Perceptron neural network in conjunction with the detection of uncertainty via Rough Sets in modeling a system that incorporates uncertainty to produce a classification of cases as positive for the disease, negative for the disease, or diagnostically uncertain. This system would serve as support for the rapid and efficient triage of cases, particularly those classified as “uncertain,” for a medical committee of specialists in a video conference, for example. Experiments were carried out and the trained model achieved 87.61% overall accuracy and a hit rate consistent across all classes.