Rostami, M., & Oussalah, M. (2022). A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest. Informatics in Medicine Unlocked, 30, 100941. https://doi.org/10.1016/j.imu.2022.100941
A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest
|Author:||Rostami, Mehrdad1; Oussalah, Mourad1,2|
1Centre for Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu, Oulu, Finland
2Research Unit of Medical Imaging, Physics, and Technology, Faculty of Medicine, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 5.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022090957986
|Publish Date:|| 2022-09-09
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.
Informatics in medicine unlocked
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This research is connected to the DigiHealth-project, a strategic profiling project at the University of Oulu. The project is supported by the Academy of Finland (project number 326291) and the University of Oulu Academy of Finland Profi5 on Digihealth.
© 2022 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).