Article
Prediction of Hypertension & Healthcare System using ML
Hypertension is one of the most common chronic health conditions and a major risk factor for cardiovascular diseases worldwide. Early detection and continuous monitoring are essential to prevent severe complications such as stroke, heart attack, and kidney failure. Traditional diagnosis relies on periodic blood pressure measurements and clinical assessment, which may miss early warning signs. This project proposes a machine learning-based healthcare system for the prediction of hypertension using patient health data. The system analyzes clinical parameters such as age, body mass index (BMI), blood pressure readings, cholesterol levels, lifestyle factors, and family history. Data preprocessing ensures quality and consistency of medical records. Multiple machine learning algorithms are trained to classify individuals as hypertensive or non-hypertensive. Feature selection improves model accuracy and efficiency. The system provides early risk prediction and decision support for healthcare professionals. Performance is evaluated using accuracy, precision, recall, and F1-score. Automated alerts notify users of potential hypertension risks. The model supports preventive healthcare and personalized treatment planning. Secure data handling ensures patient privacy. The proposed approach enhances accessibility, accuracy, and efficiency in hypertension management.
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