Development of Ensemble Learning Algorithms for Pulmonary Disease Detection System

The rising global cases of pulmonary diseases require new diagnostic methods beyond traditional techniques because they depend on subjective human analysis which proves inefficient to the healthcare system. This research solves these problems through a sophisticated ensemble learning model which unites CNNs with multimodal clinical information to boost disease recognition and identification capabilities. The method applies hierarchical extraction to both medical imaging features and structured clinical variables thus enabling the system to detect complex patterns linked to different pulmonary conditions. The framework addresses biases and improves its ability to work with different patient data through a dense neural network classifier which consolidates collected features. This research addresses major deficiencies in current methods because imaging and non-imaging data integration does not exist effectively while presenting a solution to unify these data types. Experimental results demonstrate significant potential for improving diagnostic accuracy, reducing false positives, and enabling scalable clinical decision support systems, ultimately enhancing patient outcomes and optimizing resource utilization in healthcare settings.