Study and Analysis the Influence of Immunity Factors of Patient due to Covid 19 using Machine Learning Techniques based on Logistic Regression, Decision Trees and Random Forest - Kaur, Jaspreet; Bansal, Khushboo (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This research presents a comprehensive analysis of COVID-19 patient data to predict the risk levels associated with various immunity factors. Utilizing a robust dataset provided by the Mexican government, we employed exploratory data analysis to understand the intricate relationships between patient characteristics and COVID-19 severity. Machine learning models, including Logistic Regression, Decision Trees, and Random Forest classifiers, were developed and evaluated using precision, recall, F1 score, and ROC-AUC score. The results demonstrate the effectiveness of these models in identifying high-risk patients, which could significantly aid in the strategic allocation of medical resources. The study underscores the potential of machine learning in enhancing pandemic response through informed decision-making. Future research directions include refining models with larger, more diverse datasets and integrating advanced predictive analytics for real-time risk assessment.