Pneumonia Prediction and Decision Support System
[1] Sudharshan M
Department of Artificial Intelligence and Data Science,
Sri Venkateswaraa College of Technology,
sudharshan1504@gmail.com
[2] Karthikeyan S
Department of Artificial Intelligence and Data Science, Sri Venkateswaraa College of Technology,
sskartik2004@gmail.com
[3] Narmadha V
Department of Artificial Intelligence and Data Science, Sri Venkateswaraa College of Technology,
narmadhaa1220@gmail.com
[4] Mohan A
Department of Artificial Intelligence and Data Science, Sri Venkateswaraa College of Technology,
mohan.avudayappan@gmail.com
[5] Hemalatha B
Assistant Professor, Department of Artificial Intelligence and Data Science, Sri Venkateswaraa College of Technology,
Hemasabitha1411@gmail.com
Abstract
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.
Keywords— Pneumonia Prediction, Deep Learning, Multimodal Fusion, CNN, Random Forest, Healthcare Automation
