Artificial intelligence for diagnosis of mild-moderate COVID-19 using haematological markers

Abstract

Objective: The persistent spread of SARS-CoV-2 makes diagnosis challenging because COVID-19 symptoms are hard to differentiate from those of other respiratory illnesses. The reverse transcription-polymerase chain reaction test is the current golden standard for diagnosing various respiratory diseases, including COVID-19. However, this standard diagnostic method is prone to erroneous and false negative results (10% -15%). Therefore, finding an alternative technique to validate the RT-PCR test is paramount. Artificial intelligence (AI) and machine learning (ML) applications are extensively used in medical research. Hence, this study focused on developing a decision support system using AI to diagnose mild-moderate COVID-19 from other similar diseases using demographic and clinical markers. Severe COVID-19 cases were not considered in this study since fatality rates have dropped considerably after introducing COVID-19 vaccines.

Methods: A custom stacked ensemble model consisting of various heterogeneous algorithms has been utilized for prediction. Four deep learning algorithms have also been tested and compared, such as one-dimensional convolutional neural networks, long short-term memory networks, deep neural networks and Residual Multi-Layer Perceptron. Five explainers, namely, Shapley Additive Values, Eli5, QLattice, Anchor and Local Interpretable Model-agnostic Explanations, have been utilized to interpret the predictions made by the classifiers.

Results: After using Pearson's correlation and particle swarm optimization feature selection, the final stack obtained a maximum accuracy of 89%. The most important markers which were useful in COVID-19 diagnosis are Eosinophil, Albumin, T. Bilirubin, ALP, ALT, AST, HbA1c and TWBC.

Conclusion: The promising results suggest using this decision support system to diagnose COVID-19 from other similar respiratory illnesses.

Keywords: Artificial intelligence; COVID-19; clinical markers; decision support system; machine learning.

Figure 1.

COVID-19 transmission and recovery.

Figure 2.

Box plots for four features. (a) Patient age, (b) neutrophil count, (c) lymphocyte count and (d) eosinophil count.

Figure 3.

(a) Scatter plot for neutrophil and lymphocyte, (b) scatter plot for albumin and protein and (c) bar graph for gender representation.

Figure 4.

Features chosen by various algorithms.

Figure 5.

Custom stacking architecture to predict COVID-19 diagnosis.

Figure 6.

Machine learning pipeline for COVID-19 diagnosis.

Figure 7.

AUC curves obtained by the final stack for the test dataset. (a) Pearson’s correlation, (b) cuckoo search algorithm, (c) mutual information and (d) particle swarm optimization.

Figure 8.

Precision–recall curves obtained by the final stack for the test dataset. (a) Pearson’s correlation, (b) cuckoo search algorithm, (c) mutual information and (d) particle swarm optimization.

Figure 9.

Accuracy curves obtained by the deep learning models. (a) DNN, (b) 1D-CNN, (c) LSTM and (d) ResMLP.

Figure 10.

Loss curves obtained by the deep learning models. (a) DNN, (b) 1D-CNN, (c) LSTM and (d) ResMLP.

Figure 11.

Beeswarm SHAP plots for the final stacked model. (a) Pearson’s correlation, (b) cuckoo search algorithm, (c) mutual information and (d) particle swarm optimization.

Figure 12.

SHAP dependence plots for individual patient prediction. (a) Pearson’s correlation (COVID-19 positive), (b) cuckoo search algorithm (COVID-19 positive), (c) mutual information (COVID-19 positive) and (d) particle swarm optimization (COVID-19 negative).

Figure 13.

XAI using LIME. (a) Pearson’s correlation, (b) cuckoo search algorithm, (c) mutual information and (d) particle swarm optimization.

Figure 14.

XAI using Eli5 for the decision tree model. (a) Pearson’s correlation, (b) cuckoo search algorithm, (c) mutual information and (d) particle swarm optimization.

Figure 15.

Model explainability using Qgraphs. (a) Pearson’s correlation, (b) cuckoo search algorithm, (c) mutual information and (d) particle swarm optimization.