Machine Learning-Based Blood Pressure Prediction Using Cardiovascular Disease Data: A Comprehensive Comparative Study

Abstract

Hypertension remains one of the most pressing public health challenges worldwide, affecting more than one billion individuals and serving as a principal risk factor for cardiovascular morbidity and mortality. Whilst blood pressure measurement constitutes a routine component of clinical practice, the capacity to predict blood pressure values from readily obtainable patient characteristics could substantially enhance preventive care strategies and facilitate timely intervention. The present study examines whether machine learning methodologies can reliably forecast blood pressure measurements utilizing cardiovascular risk factors in conjunction with demographic and anthropometric data. We have analyzed data from 68,616 individuals following rigorous quality assessment of 70,000 patient records obtained from Kaggle’s cardiovascular disease repository. Beyond the 10 original variables, we engineered additional features encompassing demographic patterns, body composition indices, clinical risk indicators, and their interactions. Nine distinct predictive models were systematically evaluated, spanning from elementary baseline approaches through to sophisticated gradient boosting ensembles. CatBoost demonstrated superior performance, yielding systolic blood pressure predictions with a root mean squared error (RMSE) of 14.37 mmHg and coefficient of determination (R2) of 0.265, alongside diastolic blood pressure predictions with RMSE of 8.57 mmHg and R2 of 0.187. These modest explained variance values—substantially below unity—reveal a fundamental limitation: blood pressure proves remarkably resistant to prediction from the demographic, anthropometric, and clinical variables typically available in epidemiological datasets. These findings illuminate a sobering reality regarding blood pressure prediction from routinely collected clinical data. The observation that standard variables account for merely one-quarter of blood pressure variance should temper expectations for machine learning applications within this domain, whilst simultaneously underscoring the necessity for richer data sources or novel biomarkers to achieve clinically meaningful predictive accuracy.

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