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
Keywords:
COVID-19, Logistic Regression, Decision Trees, Random Forest, Machine LearningAbstract
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.
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