Machine Learning-Based Prediction of COVID-19 Patient Outcomes: Enhancing Clinical Decision-Making

Abstract

The COVID-19 epidemic has created unprecedented challenges to healthcare systems around the world, demanding the development of reliable techniques for predicting patient outcomes to guide clinical decisions. This paper comprehensively explores machine learning-based methodologies for predicting COVID-19 patient outcomes. Using a broad dataset that includes demographic information, symptoms, comorbidities, laboratory test results, and imaging data, we use cutting-edge machine-learning approaches to create prediction models. Data preprocessing techniques including feature engineering and selection are applied to enhance model performance and reliability. Various machine learning algorithms, support vector machine, logistic regression, support and Naïve Bayes, are evaluated for their efficacy in predicting disease severity, hospitalization, and mortality outcomes. Model performance is assessed and evaluated using standard evaluation criteria, assuring the robustness of all models. These models provide useful insights into COVID-19 patient prognosis and can assist healthcare staff in triaging patients, optimizing treatment regimens, and allocating resources more efficiently. Ethical concerns around patient data privacy and model interpretability are thoroughly followed throughout the study. In all, this work highlights the promise of machine learning techniques for improving COVID-19 patient care and public health response efforts.