Evaluating the Efficacy of Deep Learning Models in Healthcare Conversations

Conversational agents are critical in the healthcare industry for increasing communication and patient experiences. This study intends to examine the efficacy of deep learning models in understanding and creating context-aware responses in healthcare-related interactions by utilizing the capabilities of these models. Sentiment analysis is critical for understanding public opinion and input, especially in the healthcare sector. We propose a novel Rule-based Sentiment Deep Belief Network (RbSdBN) model for sentiment analysis in the healthcare domain in this study. To efficiently capture sentiment patterns and explicit rules linked to healthcare, the RbSdBN model mixes rule-based reasoning, sentiment analysis, and deep belief networks. For improved sentiment classification, the model leverages a sentiment lexicon and feature selection approaches. In terms of accuracy, precision, recall, and F-score, experimental results show that the RbSdBN model outperforms baseline techniques such as Flatten-FFNN, Random Forest, SVM, and Decision Tree. The suggested model gives useful insights into sentiment analysis in the healthcare domain and can help healthcare organizations improve patient satisfaction, feedback analysis, and decision-making.