On the explanation of COVID-19 blood test variables using fuzzy models

Abstract

This paper performs an analysis comparing two evolutionary explainable fuzzy models that make inferences in a pipeline with a blood test data set for COVID-19 classification. Firstly, data is preprocessed by the following stages: cleaning, imputation and ranking feature selection. Later, we perform a comparative analysis between several clustering methods used in an Evolutionary Clustering-Structured Fuzzy Classifier (ECSFC) to solve this classification problem using the Differential Evolution (DE) algorithm. Complementarily, we find that the Fuzzy Decision Tree model produces similar performance when is tuned with the DE algorithm (EFDT). The obtained results show that, simpler models are easier to explain qualitatively, i.e., increasing the number of clusters in ECSFC model or the maximum depth of the tree in EFDT model, does not necessarily help to obtain simplified and accurate models. In addition, although the EFDT model is by itself an intuitively explainable model, the ECSFC, with the help of the proposed Weighted Stacked Features Plot, generates more intuitive models that allow not only highlighting the features and the linguistic terms that defines a patient with COVID-19, but also allows users to visualize in a single graph and in specific colors the analyzed classes.