A Brief Review on Classification of Patients with High Blood Pressure Using Machine Learning Algorithms

Abstract

Blood pressure is the force and pressure that the blood exerts on the walls of the vessels when it flows through the vessels, and it is not a problem on its own. One of the important diseases is high blood pressure, which is caused by various factors. Many patients with high blood pressure (or hypertension) do not control their disease. As a person ages, blood pressure naturally increases. Blood pressure is proportional to dietary and behavioral habits, excitement, and stress, and even changes during the hours of the day and night. Today, the use of machine learning algorithms is widely increasing to classify patients with high blood pressure. This paper conducts a succinct investigation into the application of machine learning algorithms for the classification of individuals with high blood pressure, drawing on a comprehensive analysis of existing research in the field. The machine learning algorithms considered are categorized into three distinct groups: unsupervised learning, supervised learning, and reinforcement learning. While the majority of studies have traditionally concentrated on the analysis of at least one performance criterion, a limited number have ventured into the exploration of multiple criteria.