Revolutionalizing Lung Disease Prediction Using Artificial Intelligence

The integration of Artificial Intelligence (AI) into healthcare systems holds great promise for alleviating the burden of lung diseases on individuals and society. This study contributes to the growing body of evidence that supports the crucial role of AI in healthcare particularly in the prediction of lung diseases thereby potentially saving lives and advancing personalized medicine. The research investigates the potential of Machine Learning and Deep Learning models in accurately predicting and classifying lung diseases. By utilizing features extracted from chest X-ray images and Electronic Medical Records (EMR) data, the K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms demonstrate impressive performance in identifying lung diseases. Furthermore, a multi-stacking ensemble algorithm which combines KNN, SVM and CNN surpasses individual models achieving an outstanding accuracy of 98.3% and favorable evaluation metrics. These findings emphasize the importance of advanced computational techniques in revolutionizing healthcare practices enabling early detection, timely interventions, and improved patient outcomes. Besides practical considerations such as the feasibility of real-time deployment and computational efficiency need to be addressed for effective implementation in clinical settings.