PREDCTION OF HYPER TENSION USING MACHINE LEARNING

Abstract

Hypertension is a major health issue that can lead to severe complications such as heart disease, stroke, and kidney failure if not detected early. This project proposes a machine learning-based system to predict hypertension using patient health data. The system analyzes parameters such as blood pressure, age, BMI, lifestyle habits, and medical history to classify individuals into different blood pressure categories. Various machine learning algorithms such as Decision Tree, Random Forest, Support Vector Machine (SVM), and Logistic Regression are implemented and evaluated. The performance of these models is measured using accuracy, precision, recall, and F1-score. Among all models, the Decision Tree algorithm shows high accuracy and interpretability, making it suitable for medical prediction. The system also includes an alert mechanism that notifies medical professionals when a patient is predicted to be at risk. The proposed approach provides an efficient, scalable, and reliable solution for early detection of hypertension, helping in timely diagnosis and prevention.