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


  • Jaspreet Kaur Research scholar, Desh bhagat university Mandi gobindgarh, Punjab, India
  • Khushboo Bansal Assistant Professor, Desh bhagat university Mandi gobindgarh, Punjab India


COVID-19, Logistic Regression, Decision Trees, Random Forest, Machine Learning


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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How to Cite

Kaur, J. ., & Bansal, K. . (2024). 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 . International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 402 –. Retrieved from



Research Article