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. 2021 Apr 13;9(4):e25884. doi: 10.2196/25884

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©Sakifa Aktar, Md Martuza Ahamad, Md Rashed-Al-Mahfuz, AKM Azad, Shahadat Uddin, AHM Kamal, Salem A Alyami, Ping-I Lin, Sheikh Mohammed Shariful Islam, Julian MW Quinn, Valsamma Eapen, Mohammad Ali Moni. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 13.04.2021.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

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Sorted significant and impacted blood parameters of patients with COVID-19 based on SHAP values, defined as the coefficient values of each parameter after model training: (A) combined patients group; (B) patients with noncommunicable diseases; (C) patients without noncommunicable diseases. Artificial intelligence models were used to identify the most predictive blood parameters for the severity of COVID-19 symptoms. Higher coefficient values of machine learning model outcomes indicate a higher significant association with disease severity. D-dimer: dimerized plasmin fragment D; FFA: free fatty acids; GGT: gamma-glutamyl transferase; INR: international normalized ratio; SHAP: Shapley Additive Explanations; TTPA: partial thromboplastin time.