CovidXAI: explainable AI assisted web application for COVID‐19 vaccine prioritization

COVID‐19 vaccines have a limited supply, and there is a huge gap between supply and demand, leading to disproportionate administration. One of the main conditions on which balanced and optimal vaccine distribution depends are the health conditions of the vaccine recipients. Vaccine administration of front‐line workers, the elderly, and those with diseases should be prioritized. To solve this problem, we proposed a novel architecture called CovidXAI, which is trained with a self‐collected dataset with 24 parameters influencing the risk group of the vaccine recipient. Then, Random Forest and XGBoost classifiers have been used to train the model—having training accuracies of 0.85 and 0.87 respectively, to predict the risk factor, classified as low, medium, and high risk. The optimal vaccine distribution can be done using the derived from the predicted risk class. A web application is developed as a user interface, and Explainable AI (XAI) has been used to demonstrate the varying dependence of the various factors used in the dataset, on the output by CovidXAI. To ease the usage of the prototype, a web‐application is developed. A machine learning model is used in the prototype to predict the risk group of the user based on the given input. Based on the risk factor, the user is classified into low, medium or high risk group. The urgency of vaccination of the user hence can be derived from the predicted risk group. To make the working of the model clear, the usage of XAI is done to show the impact of each of the 24 parameters on the outputted risk factor.

CovidXAI: Explainable AI Assisted Web Application for COVID-19 Vaccine Prioritisation

COVID-19 vaccines have a limited supply, and there is a huge gap between supply and demand, leading to disproportionate administration. One of the main conditions on which balanced and optimal vaccine distribution depends are the health conditions of the vaccine recipients. Vaccine administration of front-line workers, the elderly, and those with diseases should be prioritized. To solve this problem, we proposed a novel architecture called CovidXAI, which is trained with a self-collected dataset with 24 parameters influencing the risk group of the vaccine recipient. Then, Random Forest and XGBoost classifiers have been used to train the model ? having training accuracies of 0.85 and 0.87 respectively, to predict the risk factor, classified as low, medium, and high risk. The optimal vaccine distribution can be done using the derived from the predicted risk class. A web application is developed as a user interface, and Explainable AI (XAI) has been used to demonstrate the varying dependence of the various factors used in the dataset, on the output by CovidXAI. This article is protected by copyright. All rights reserved.