Predictive Pulse: Harnessing Machine Learning for Blood Pressure Analysis

Hypertension is a major global health concern and a primary risk factor for cardiovascular morbidity and mortality. Traditional clinical approaches rely on episodic blood pressure (BP) measurements and fixed diagnostic thresholds, limiting their ability to anticipate transient but clinically significant BP spikes that may precede acute cardiovascular events. This paper presents Predictive Pulse, an end-to-end and interpretable machine learning framework for early prediction of blood pressure spike risk using routinely collected clinical, demographic, and lifestyle data. The proposed system incorporates clinically informed data preprocessing, structured feature engineering, exploratory data analysis, and supervised learning to enable proactive cardiovascular risk assessment. Blood pressure values expressed as categorical ranges are systematically transformed into numeric representations to preserve physiological relevance while facilitating quantitative modeling. Multiple classification models, including Logistic Regression, Random Forests, Gradient Boosting, and Support Vector Machines, are evaluated under consistent validation protocols. Experimental results demonstrate that ensemble-based models achieve superior predictive performance compared to linear baselines, effectively capturing non-linear interactions among physiological and behavioral factors. The framework emphasizes interpretability and robustness, making it suitable for real-world preventive healthcare applications. By shifting from static hypertension classification to anticipatory blood pressure spike prediction, this work contributes a scalable and reproducible approach that supports early intervention and advances data-driven preventive cardiovascular analytics