Pneumonia Prediction and Decision Support System

The rising prevalence of pneumonia demands automated diagnostic systems to enhance clinical efficiency and accuracy. Traditional diagnosis, reliant on manual chest X-ray and blood test analysis, is time-consuming and error-prone. This project introduces a deep learning-based system for pneumonia prediction, integrating chest X-ray images and blood test biomarkers to classify patients as healthy, viral, or bacterial pneumonia. It employs Convolutional Neural Networks (CNNs) for X-ray feature extraction, Random Forests for biomarker classification, and a heuristic-based fusion model for accurate predictions. Preprocessing includes image normalization and biomarker validation, with Grad-CAM enhancing interpretability. The system achieves 93.4% fusion accuracy, processes cases in 3.8 seconds, and generates structured clinical reports. Scalable and web-based, it supports paperless healthcare and hospital integration. Future enhancements include multilingual support and cloud deployment, advancing digital transformation in medical diagnostics.