COVID-19 diagnosis from routine blood tests using artificial intelligence techniques. (February 2022)

• Record Type: Journal Article
• Authors: Babaei Rikan, Samin; Sorayaie Azar, Amir; Ghafari, Ali; Bagherzadeh Mohasefi, Jamshid; Pirnejad, Habibollah
• Is Part Of: Biomedical signal processing and control. Volume 72(2022)Part A
• Publication Date: 2022-02
• Subjects: COVID-19 -- Blood tests -- Diagnosis -- Machine learning -- Deep learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103263
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Early diagnosis of COVID-19 disease using a novel DNN developed using various datasets. Developed light-weights DNN model outperforms similar models with higher number of parameters. Introducing important features for the DNN when making a decision and their relevancy using different methods. Comparing performance of the developed model with models introduced in other studies using paired-sample T-Test. Abstract: Coronavirus disease (COVID-19) is a unique worldwide pandemic. With new mutations of the virus with higher transmission rates, it is imperative to diagnose positive cases as quickly and accurately as possible. Therefore, a fast, accurate, and automatic system for COVID-19 diagnosis can be very useful for clinicians. In this study, seven machine learning and four deep learning models were presented to diagnose positive cases of COVID-19 from three routine laboratory blood tests datasets. Three correlation coefficient methods, i.e., Pearson, Spearman, and Kendall, were used to demonstrate the relevance among samples. A four-fold cross-validation method was used to train, validate, and test the proposed models. In all three datasets, the proposed deep neural network (DNN) model achieved the highest values of accuracy, precision, recall or sensitivity, specificity, F1-Score, AUC, and MCC. On average, accuracy 92.11%, specificity 84.56%, and AUC 92.20% values have been obtained in the first dataset. In the second dataset, on average, accuracy 93.16%, specificity 93.02%, and AUC 93.20% values have been obtained. Finally, in the third dataset, on average, the values of accuracy 92.5%, specificity 85%, and AUC 92.20% have been obtained. In this study, we used a statistical t -test to validate the results. Finally, using artificial intelligence interpretation methods, important and impactful features in the developed model were presented. The proposed DNN model can be used as a supplementary tool for diagnosing COVID-19, which can quickly provide clinicians with highly accurate diagnoses of positive cases in a timely manner.