Optimizing Pneumonia Diagnosis during COVID-19: Utilizing Random Forest for Accurate Classification and Effective Public Health Interventions

Abstract

In the quest for precise diagnosis and classification of pneumonia, particularly intensified by the Coronavirus 2019 (COVID-19) pandemic, this research work presents an optimized Random Forest algorithm based mechanism specifically tailored for COVID-19 pneumonia classification. The research methodology encompasses four critical phases: data acquisition from a current COVID-19 X-ray image dataset on GitHub, data processing and analysis using histograms and scatter plots, application of supervised learning with Random Forest enhanced by data augmentation techniques, and performance evaluation through comparative analysis with existing methods. Our proposed model achieved an accuracy score of 82.29% on average, demonstrating significant precision and recall capabilities. Results indicate that the Random Forest model outperforms current methodologies, providing a robust framework for future pneumonia classification research. This study underscores the potential for improved diagnostic accuracy and patient care, highlighting the model's utility in supporting public health interventions and optimizing resource allocation in the context of COVID-19 pneumonia.