Graph embedding dimensionality reduction combined with improved APO optimized kELM for pneumonia recognition

Pneumonia is a significant global public health concern, and accurate recognition is essential for improving global health outcomes. In this study, we propose an improved Artificial Protozoa Optimizer Kernel Extreme Learning Machine (iAPO-kELM) combined with Graph Embedding Dimensionality Reduction (GEDR) method for pneumonia detection. Specifically, we reduce the dimension of lung X-ray image data based on Graph Embedding Extreme Learning Machine (ELM). To enhance recognition efficiency and accuracy, the reduced-dimensionality data is prioritized, and the APO algorithm is improved to optimize the parameters of the kELM algorithm. Additionally, we study the kELM classification performance for pneumonia using different kernel functions. To validate the superiority and reliability of the proposed method, we compare it with other optimization algorithms, dimensionality reduction methods, and classification algorithms. Multiple evaluation metrics, including Precision, Recall, and F1 score, are used for assessment. The results of the five-fold cross-validation experiment show that iAPO-kELM combined with GEDR achieves Precision, Recall, and F1 scores of 0.9716, 0.9714, and 0.9715, respectively, outperforming competitive algorithms. These findings suggest that the proposed approach can assist radiologists in diagnosing pneumonia from chest X-ray images effectively.