A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Mehrdad Rostami; Mourad Oussalah

doi:10.1016/j.imu.2022.100941

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Inform Med Unlocked. 2022:30:100941. doi: 10.1016/j.imu.2022.100941. Epub 2022 Apr 6.

Authors

Mehrdad Rostami¹, Mourad Oussalah^{1

2}

Affiliations

¹ Centre for Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu, Oulu, Finland.
² Research Unit of Medical Imaging, Physics, and Technology, Faculty of Medicine, University of Oulu, Finland.

Abstract

Several Artificial Intelligence-based models have been developed for COVID-19 disease diagnosis. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered diagnosis and the potential future of machine-centered disease diagnosis. Under the concept of human-computer interaction design, this study proposes a new explainable artificial intelligence method that exploits graph analysis for feature visualization and optimization for the purpose of COVID-19 diagnosis from blood test samples. In this developed model, an explainable decision forest classifier is employed to COVID-19 classification based on routinely available patient blood test data. The approach enables the clinician to use the decision tree and feature visualization to guide the explainability and interpretability of the prediction model. By utilizing this novel feature selection phase, the proposed diagnosis model will not only improve diagnosis accuracy but decrease the execution time as well.

Keywords: COVID-19; Decision forest; Disease diagnosis; Explainable artificial intelligence; Feature selection; Human-computer interaction.