Abstract

Interpretable machine learning models play a vital role in providing explanations for their predictions, ensuring user trust and confidence in various domains. While traditional machine learning measurements like AUC, precision, and recall are commonly used, they may not suffice when trust in machine learning systems' predictions is paramount. To address this, sentiment analysis, leveraging natural language processing, has been employed to comprehend human emotional responses in written language. This research explores the innovative combination of natural language processing and sentiment analysis to infer human emotions from text. Additionally, it discusses the potential errors of artificial intelligence (AI) systems in healthcare and emphasizes the importance of studying both the positive advancements and potential negative impacts. By considering user trust and interpretability, this research aims to foster the development of sustainable healthcare monitoring using AI and machine learning. The keywords for this research are AI, machine learning, sentiment analysis, and healthcare monitoring.

Description