Diagnosing COVID-19 with Bayesian Networks: A Streamlit Application
Introduction
The COVID-19 pandemic has underscored the importance of rapid and accurate diagnostic tools. Traditional diagnostic methods, such as PCR tests, though reliable, can be time-consuming and resource-intensive. In response to this challenge, we explore a probabilistic approach to diagnosing COVID-19 based on observable symptoms using Bayesian networks. Leveraging data from the World Health Organization (WHO), we construct a Bayesian network model and develop a user-friendly interface with Streamlit to facilitate instant preliminary diagnoses.
Problem Statement
Our goal is to develop a Bayesian network model that can diagnose COVID-19 infection based on symptoms such as fever, cough, and fatigue. This model will be implemented using Python and will include a Streamlit application to allow users to input symptoms and receive an immediate diagnosis probability.
Understanding Bayesian Networks
Bayesian networks are graphical models that represent the probabilistic relationships among a set of variables. They are particularly useful in medical diagnosis because they can handle uncertainty and incorporate both prior knowledge and new evidence. In the context of COVID-19, a Bayesian network can model the relationships between symptoms (e.g., fever, cough, fatigue) and the likelihood of infection.
Steps to Build the Model
- Data Acquisition: We begin by obtaining a dataset from WHO that includes information on COVID-19 diagnoses and associated symptoms. This dataset is crucial for training our Bayesian network model.
- Data Preprocessing: The dataset is cleaned and preprocessed to ensure it is suitable for model building. This involves handling missing values, encoding categorical variables, and normalizing the data if necessary.
- Model Construction: We define the structure of the Bayesian network, specifying nodes for symptoms and the COVID-19 diagnosis, and edges representing the probabilistic dependencies between them.
- Probability Estimation: Using the dataset, we calculate the conditional probabilities required for the Bayesian network. This step involves determining the likelihood of each symptom given the presence or absence of COVID-19.
- Implementation: We implement the Bayesian network using the
pgmpylibrary in Python. This library provides tools for creating and querying probabilistic graphical models. - User Interface Development: To make the model accessible, we develop a Streamlit application. This web-based interface allows users to input their symptoms and receive an instant probability of COVID-19 infection.
Implementing the Bayesian Network
The core of our implementation involves constructing the Bayesian network and estimating the required probabilities.
Our Streamlit application provides a simple and interactive way for users to input their symptoms and receive a diagnosis probability. The user interface allows users to select whether they have symptoms such as fever, cough, and fatigue, and then calculates the probability of COVID-19 infection based on these inputs.
Conclusion
By leveraging Bayesian networks and the power of probabilistic modeling, we can create a quick and efficient tool for preliminary COVID-19 diagnosis. This model, implemented with a user-friendly Streamlit application, provides an accessible means for individuals to assess their likelihood of infection based on symptoms. While this tool is not a replacement for professional medical diagnosis, it can serve as a valuable preliminary screening tool, potentially guiding users towards seeking further medical advice and testing.