Prediction of Hypertension Patients with Machine Learning Algorithm

Hypertension, known as the "silent killer," is one of the leading causes of global mortality, with a steadily increasing prevalence. Worldwide, the prevalence of hypertension reaches approximately 30%, with only 50% of cases being diagnosed and a low level of treatment adherence. Hypertension symptoms are often invisible, making early detection crucial to preventing serious complications. This paper aims to develop a hypertension prediction system using the Decision Tree and Random Forest algorithms, which are machine learning techniques used to solve classification and regression problems. These algorithms can predict hypertension risk based on clinical data, such as age, medical history, and lifestyle. The findings of this paper indicate that the Decision Tree and Random Forest algorithms are effective in predicting hypertension risk, achieving accuracies of 99.6% and 99.5%, respectively. This prediction system can provide fast and accurate information, assisting healthcare professionals in designing appropriate intervention strategies while also supporting better medical decision-making.