# Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression

arXiv: https://arxiv.org/abs/2006.16942 · HTML: https://mlsanity.com/paper/2006.16942/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models](https://mlsanity.com/paper/2103.00747/) (2021)
- [Multiwave COVID-19 Prediction from Social Awareness using Web Search and Mobility Data](https://mlsanity.com/paper/2110.11584/) (2021)
- [Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility](https://mlsanity.com/paper/2409.12116/) (2024)

## Abstract (arXiv)

Amid the ongoing COVID-19 pandemic, whether COVID-19 patients with high risks can be recovered or not depends, to a large extent, on how early they will be treated appropriately before irreversible consequences are caused to the patients by the virus. In this research, we reported an explainable, intuitive, and accurate machine learning model based on logistic regression to predict the fatality rate of COVID-19 patients using only three important blood biomarkers, including lactic dehydrogenase, lymphocyte (%) and high-sensitivity C-reactive protein, and their interactions. We found that when the fatality probability produced by the logistic regression model was over 0.8, the model had the optimal performance in that it was able to predict patient fatalities more than 11.30 days on average with maximally 34.91 days in advance, an accumulative f1-score of 93.76% and and an accumulative accuracy score of 93.92%. Such a model can be used to identify COVID-19 patients with high risks with three blood biomarkers and help the medical systems around the world plan critical medical resources amid this pandemic.