A Tree-based Mortality Prediction Model of COVID-19 from Routine Blood Samples

Nunung Nurul Qomariyah, Ardimas Andi Purwita, Sri Dhuny Atas Asri, Dimitar Lubomirov Kazakov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

COVID-19 has been declared by The World Health Organization (WHO) a global pandemic in January, 2020. Researchers have been working on formulating the best approach and solutions to cure the disease and help to prevent such pandemics in the future. A lot of efforts have been made to develop a fast and accurate early clinical assessment of the disease. Machine Learning (ML) has proven helpful for research and applications in the health domain as a way to understand real-world phenomena through data analysis. In our experiment, we collected the retrospective blood samples data set from 1,000 COVID-19 patients in Jakarta, Indonesia for the period of March to December 2020. We report our preliminary findings on the use of common blood test biomarkers in predicting COVID-19 patient mortality. This study took advantage of explainable machine learning to examine the data set. The contribution of this paper is to explain our findings on predicting COVID-19 mortality, including the role of the top 11 biomarkers found in our dataset. These findings can be generalized, especially in Indonesia, which is now at its highest peak of the epidemic. We show that tree-based AI models performed well on predicting COVID-19 mortality, while also making it easy to interpret the findings, as they lend themselves to human scrutiny and allow clinicians to interpret them and comment on their viability.
Original languageEnglish
Title of host publication2021 International Conference on ICT for Smart Society (ICISS), Proc. of
PublisherIEEE
Number of pages7
DOIs
Publication statusPublished - 13 Sep 2021

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