Artificial Intelligence approaches for Predicting Hypertension Diseases: Open Challenges and Research Issues

Despite improved therapies and decades of research over the last century, hypertension remained the world's leading avoidable cause of death. Machine learning (ML) is a subfield of artificial intelligence that integrates computer science, statistics, and decision theory to recognize complicated patterns in vast amounts of data. Because hypertension may induce a rapid rise in blood pressure, artificial intelligence approaches are used to anticipate the start of hypertension The study's main goal is to use artificial intelligence technologies like deep learning (DL) and machine learning (ML) to predict hypertension. This research will analyze the current state, challenges, and potential of adopting technologies to detect and predict hypertension diseases. Thus, AI-integrated hypertension care has the ability to revolutionize clinical practice by introducing individualized approaches to prevention and treatment, such as the establishment of optimal and patient-specific blood pressure (BP) goals, and the development of interventions focusing on modifiable risk factors. Prior research has showed that machine learning has the ability to improve all areas of patient care, from research and development to everyday clinical practice and global health. The purpose of this paper is to illustrate the potential of artificial intelligence for treating hypertension by analyzing existing evidence.