Predicting COVID-19 Outcomes Using Machine Learning Techniques | IJCSE Volume 9 – Issue 6 | IJCSE-V9I6P3

This research paper explores the application of machine learning techniques for predicting COVID-19 outcomes, leveraging a comprehensive literature base of 35 peer-reviewed articles. The study aims to synthesize existing methodologies, datasets, and models to propose a robust framework for accurate prediction and diagnosis. The study synthesizes existing methodologies, datasets, and models to propose a robust framework for accurate prediction and diagnosis. We implement and compare multiple ML algorithms including Random Forest, XGBoost, Support Vector Machines, Logistic Regression, on COVID-19 clinical data. Our best-performing model [XGBoost ensemble) achieved an accuracy of 91.2%, AUC- ROC of 0.94, and F1-score of 0.89 for mortality prediction. The findings emphasize the critical role of artificial intelligence in pandemic response, resource allocation, and clinical decision support systems. The abstract summarizes the objectives, methods, and key findings, emphasizing the role of artificial intelligence in pandemic response

This research demonstrates that machine learning, particularly ensemble methods combining XGBoost with Random Forest, provides highly accurate prediction of COVID-19 outcomes, achieving an AUC-ROC of 0.951 for mortality prediction. The integration of clinical data with advanced algorithms enables early diagnosis, risk stratification, and optimization of healthcare resource allocation.

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