This paper discusses the application of machine learning technology in the healthcare system, specifically in the context of heart disease detection. The study utilizes various machine learning algorithms, such as the KNN algorithm, decision tree method, and random forest algorithm, to create a predictive model for heart disease detection. The sentiment analysis method is also applied in the healthcare system for risk detection in patients. The study also identifies some of the obstacles faced in the data collection process and proposes solutions to overcome these problems. The findings of this study suggest that the application of machine learning technology and sentiment analysis in healthcare can play a significant role in the early detection and prevention of heart disease, which can help save lives and improve the quality of healthcare services

Machine learning, sentiment analysis, healthcare, brand management, python-based software, wearable devices, IoT systems, quantum-enhanced machine learning, artificial intelligence