• Saniya Raheen Patel
• Dr. Nazneen Akhter
• Dr. Bharat Naiknaware

Cardiovascular stress monitoring plays a crucial role in managing hypertensive patients and preventing complications such as stroke or cardiac failure. This paper presents a novel approach using AI-based modeling to predict cardiovascular stress by analyzing blood pressure (BP) and heart rate (HR) data. Simulated datasets representing two patient profiles (normal and abnormal) were generated over a 6-month period. We applied linear regression to model the relationship between systolic BP and HR, achieving a prediction accuracy with R² = 0.72 and MAPE = 3.51%. The system identifies high-risk episodes using co-occurrence logic (BP > 180 mmHg and predicted HR > 100 bpm), enabling early warnings. Performance was evaluated using RMSE, MAPE, and NRMSE. The results show the potential of simple, interpretable predictive models in proactive cardiovascular health monitoring.

Cardiovascular stress, blood pressure, heart rate, Predictive Models in healthcare, linear regression, patient monitoring, MAPE, RMSE, simulated data.

"A Simulated Case Study for Application of Blood Pressure Parameters to predict Heart Rate", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.g582-g585, July-2025, Available :http://www.jetir.org/papers/JETIR2507684.pdf

"A Simulated Case Study for Application of Blood Pressure Parameters to predict Heart Rate", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppg582-g585, July-2025, Available at : http://www.jetir.org/papers/JETIR2507684.pdf