Pneumonia Disease Assessment with Full Diagnostics Evaluation

Pneumonia remains a critical global health issue, causing significant illness and mortality, especially in vulnerable populations. Early and precise diagnosis is essential for effective management and timely the intervention. This research paper introduces a novel integrated approach for the prediction and comprehensive diagnostics of pneumonia, harnessing cutting-edge advancements in medical imaging, machine learning, and clinical data analysis. The proposed framework merges three fundamental components: (1) Chest X-ray Image Analysis, (2) Clinical Data Integration, and (3) Machine Learning-Based Prediction. The chest X-ray image analysis employs state-of-the-art deep learning techniques to automatically identify and classify pneumonia-related anomalies, offering detailed insights into pulmonary conditions. Simultaneously, clinical data, encompassing patient history, demographics, and laboratory results, are integrated into the predictive model. A hybrid machine learning algorithm, combining Convolutional Neural Networks (CNN) and Gradient Boosting, is utilized to ensure robust and precise disease prediction. The research demonstrates the efficacy of this integrated approach through a thorough evaluation on a substantial dataset of pneumonia cases. Findings indicate a notable enhancement in pneumonia prediction accuracy compared to conventional diagnostic methods. Moreover, the model’s interpretability is enhanced, allowing healthcare professionals to scrutinize the rationale behind each prediction, promoting trust and transparency in the decision-making process.