Annals of Medicine.

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

Krishnaraj Chadaga, Srikanth Prabhu, Vivekananda Bhat, Niranjana Sampathila, Shashikiran Umakanth, and Rajagopala Chadaga. Annals of Medicine (April 2023).



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.


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.


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.


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


Try the QLattice.

Experience the future of AI, where accuracy meets simplicity and explainability.

Models developed by the QLattice have unparalleled accuracy, even with very little data, and are uniquely simple to understand.

The QLattice

Share this publication.

The QLattice accelerates discoveries with explainable insights.​

Researchers and and scientists cite Abzu’s QLattice symbolic AI in industry-leading journals for introducing a new standard of performance and explainability to data sets.

Subscribe for
notifications from Abzu.

You can opt out at any time. We’re cookieless, and our privacy policy is actually easy to read.