Abstract

The medical condition known as hypertension, or high blood pressure, is characterized by persistently elevated blood pressure against the arterial walls. Generally speaking, an individual should maintain blood pressure from 120/80 mm Hg. Whenever blood pressure continuously registers at 130/80 mm Hg or above, hypertension is frequently diagnosed. The exact origins are unknown, but factors that accelerate its growth include obesity, high-stress levels, aging, increased sodium intake, and decreased physical activity. Numerous organs and systems inside the body can be significantly impacted by hypertension or high blood pressure. It can cause several major health issues and diseases, including renal disease and stroke if left unchecked and untreated. When it comes to the identification and treatment of hypertension, or high blood pressure, machine learning can be an invaluable tool. It can help medical practitioners with several procedures, such as risk evaluation, early detection, and individualized care. Decision-support tools that provide treatment suggestions based on the most recent medical research and patient-specific data are one way that machine learning can help healthcare providers. This can assist physicians in making better-informed choices regarding medication and lifestyle modifications. Patients with hypertension can benefit from individualized therapy regimens designed with the help of machine learning. A variety of machine learning algorithms are available for the prediction of hypertension and related risk variables, including decision trees (DT), Random Forests (RF), gradient boosting machines (GBM), extreme gradient boosting (XG Boost), logistic regression (LR), and linear discriminant analysis (LDA). The quality of the available dataset and the suitable technique are critical to the effectiveness of machine learning in the detection and management of hypertension.