Machine Learning for Holistic Pneumonia Management: Detection And Remediation

This study presents an integrated approach for Pneumonia detection, remedial suggestions, and air quality assessment. Leveraging machine learning techniques, the system employs Random Forest to gauge lung quality and ResNet-50 for PNEUMONIA detection via X-ray imaging analysis. The Random Forest algorithm assesses lung health by analyzing various parameters, aiding in early identification of abnormalities. Simultaneously, ResNet-50, a deep learning model, accurately detects PNEUMONIA manifestations in X-ray images, enabling swift diagnosis. Furthermore, this research investigates the correlation between air quality and PNEUMONIA prevalence, emphasizing environmental factors' impact on respiratory health. The integrated system holds promise in early PNEUMONIA detection, providing remedial suggestions based on severity levels, and highlights the critical role of air quality in respiratory health. Its multifaceted approach offers a comprehensive framework for mitigating PNEUMONIA risks and improving public health strategies