A Machine Learning (ML) based Forecasting Model for Covid-19 Patients

Abstract

This research paper introduces a Machine Learning (ML) based forecasting model for COVID-19 cases, to investigate the performance of the Machine Learning algorithms and develop a new procedure to improve prediction efficiency. Utilizing the Multilayer Perceptron (MLP), Linear Regression (LR), K-Nearest Neighbours (KNN), Support Vector Machines (SVM), and the proposed approach, the study considers the COVID-19 data to forecast case numbers. The study contextualizes its findings through a systematic methodology of dataset compilation, algorithm interpretation, and framework development. It uses measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) to evaluate the predictive performance. The trend in COVID-19 data and predictions from different algorithms is shown through graphical illustrations. The same goes for the proposed framework predictions. This study demonstrates that the proposed LR approach and the framework outperform previous MLP, KNN, and SVM models, suggesting the relevance of explainable and robust modeling solutions for COVID-19 risk assessment. Our suggested framework, which particularly outperforms individual algorithms by averaging out their results after combining them, reduces prediction errors significantly. Discussion of implications of forecasts for interventions, resource allocation, and policy decisions is done, with the need for accurate forecasting in the pandemic response highlighted. Further research works can be expected to improve the framework, incorporate new machine learning (ML) techniques, and deploy real-time adaptive modeling systems. Collaboration between researchers, policymakers, and healthcare practitioners is crucial for the adoption of research results into practice and the promotion of evidence-based decision-making in the context of outbreak response and preparedness. Generally, this study shows the progress of the field of epidemiological forecasting as well as provides information that is important in fighting COVID-19 and future epidemics.