Healthcare Data Analysis Using Capsule Networks

In the realm of healthcare data analysis, capturing complex relationships within data, particularly in unstructured formats like text, presents a significant challenge. Traditional deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), often struggle to preserve spatial hierarchies and contextual nuances, leading to suboptimal performance in tasks like multi-label classification. This paper explores the application of Capsule Networks (CapsNet) combined with the Gravitational Search Algorithm (GSA) to address these limitations. CapsNet, with its unique ability to maintain spatial relationships within data, is particularly effective in healthcare scenarios where the interpretation of structured and unstructured data is critical. The proposed CapsNet-GSA model is evaluated on healthcare-related Twitter datasets, including sentiment analysis and disease classification tasks. Results demonstrate that the model outperforms traditional deep learning approaches, achieving higher accuracy and better handling of multi-label classification problems, thereby offering a robust solution for complex healthcare data analysis.