Bayesian Networks and Machine Learning for COVID-19 Severity Explanation and Demographic Symptom Classification

• URL: http://arxiv.org/abs/2406.10807v2
• Date: Tue, 18 Jun 2024 02:20:19 GMT
• Title: Bayesian Networks and Machine Learning for COVID-19 Severity Explanation and Demographic Symptom Classification
• Authors: Oluwaseun T. Ajayi, Yu Cheng,
• Abstract summary: We present a three-stage data-driven approach to distill the hidden information about COVID-19. The first stage employs a Bayesian network structure learning method to identify the causal relationships among COVID-19 symptoms. As a second stage, the output serves as a useful guide to train an unsupervised machine learning (ML) algorithm. The final stage then leverages the labels obtained from clustering to train a demographic symptom identification model.
• Score: 12.40025057417184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the prevailing efforts to combat the coronavirus disease 2019 (COVID-19) pandemic, there are still uncertainties that are yet to be discovered about its spread, future impact, and resurgence. In this paper, we present a three-stage data-driven approach to distill the hidden information about COVID-19. The first stage employs a Bayesian network structure learning method to identify the causal relationships among COVID-19 symptoms and their intrinsic demographic variables. As a second stage, the output from the Bayesian network structure learning, serves as a useful guide to train an unsupervised machine learning (ML) algorithm that uncovers the similarities in patients' symptoms through clustering. The final stage then leverages the labels obtained from clustering to train a demographic symptom identification (DSID) model which predicts a patient's symptom class and the corresponding demographic probability distribution. We applied our method on the COVID-19 dataset obtained from the Centers for Disease Control and Prevention (CDC) in the United States. Results from the experiments show a testing accuracy of 99.99%, as against the 41.15% accuracy of a heuristic ML method. This strongly reveals the viability of our Bayesian network and ML approach in understanding the relationship between the virus symptoms, and providing insights on patients' stratification towards reducing the severity of the virus.

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