Advanced Healthcare Analytics: Pneumonia Disease Classification Using Convolutional Neural Networks and Ensemble Learning

Abstract

The need for this work arises from the persistent challenges in accurately classifying pneumonia disease. Existing methods, although valuable, have limitations in terms of precision, accuracy, recall, speed, and AUC, prompting the exploration of advanced healthcare analytics. The limitations of current approaches are evident in their suboptimal performance levels. These methods often struggle to achieve the precision and recall needed for reliable pneumonia classification, leading to misdiagnoses and delayed treatments. Additionally, their speed and AUC fall short of the ideal standards required for efficient disease detection. To address these issues, our paper presents an innovative approach process. We introduce a novel model that combines Convolutional Neural Networks (CNN) with Naive Bayes, Deep Forest, and Multilayer Perceptron techniques. This fusion of diverse methods capitalizes on their complementary strengths, enhancing the accuracy and robustness of pneumonia classification process. The advantages of our proposed model are multifold. Firstly, the CNN component leverages its deep learning capabilities to extract intricate features from medical images, improving the overall precision and recall. Secondly, the inclusion of Naive Bayes brings a probabilistic perspective, enhancing classification based on statistical likelihood. Thirdly, Deep Forest contributes with its ensemble learning prowess, adding another layer of accuracy to the model. Lastly, the Multilayer Perceptron serves as a flexible, non-linear classifier, fine-tuning the results. Our empirical results demonstrate the substantial impact of this work. When tested on diverse contextual datasets, our model exhibits significant improvements: a 4.9% increase in precision, a 5.5% boost in accuracy, a 4.5% rise in recall, a 3.9% enhancement in speed, and a 2.9% improvement in AUC compared to existing methods. These outcomes underscore the potential of our approach to revolutionize pneumonia disease classification in healthcare, ultimately leading to more accurate and timely diagnoses, which can significantly improve patient outcomes and reduce healthcare costs.