Predicted Blood Pressure using Machine Learning

High blood pressure or hypertension, is a prevalent health condition affecting a significant portion of the population. It is a major determinant factor for various cardiovascular diseases and can expedite to severe complications if left untreated. Accurate prediction of blood pressure levels plays a crucial role in early detection, prevention, and management of hypertension although it is hard for an individual to test a blood pressure on a regular basis and if though population take a blood test regularly it is hard to rely on one test this research gives a solid idea that a individual is suffering from a regular high blood pressure or hypertension. This research paper provides a comprehensive review of predictive models for blood pressure prediction. It covers various machine learning techniques like CNN, random forest, and covers future directions for further research purposes. This paper gives accuracy of 91.30% using random forest classifier. Our results also show that convolution neural network (CNN) and random forest classifier are suitable models for predicting the blood pressure.