Predicting Hypertension Through Lifestyle Analytics using Machine learning

Author(s)	Palak Shahr, Bhargvi Patel
Country	India
Abstract	Hypertension is a major risk factor for cardiovascular diseases and remains a global health concern due to its high prevalence and asymptomatic nature. Early detection is crucial for timely intervention. This study applies machine learning techniques to predict hypertension using a large dataset of 174,982 records with demographic, clinical, and lifestyle features such as age, BMI, blood pressure, cholesterol, stress, physical activity, and dietary habits. Data preprocessing included encoding categorical variables, normalization, and handling class imbalance. Models such as Random Forest, XGBoost, and Logistic Regression were trained and evaluated using accuracy, precision, recall, and F1-score. Feature importance analysis was conducted to identify key predictors. Results show that age, BMI, stress, physical activity, and diet are strongly associated with hypertension. The models achieved high predictive performance, demonstrating their potential for large-scale risk assessment. This study highlights the role of machine learning in early detection and prevention, supporting data-driven healthcare strategies to reduce cardiovascular disease burden.
Keywords	Hypertension Prediction, Machine Learning, Cardiovascular Risk, Feature Importance, Early Detection.
Field	Engineering
Published In	Volume 17, Issue 1, January-March 2026
Published On	2026-03-31

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