Real-Time Cardiovascular Risk Prediction using IoT-Based Patient Monitoring and Deep Neural Networks
DOI:
https://doi.org/10.15662/IJEETR.2026.0802366Keywords:
IoT, Cardiovascular Risk Prediction, Deep Neural Networks, Patient Monitoring, Smart Healthcare, Real-Time AnalysisAbstract
Cardiovascular diseases are one of the leading causes of death worldwide, making early detection and continuous monitoring essential for improving patient outcomes. This paper presents a real-time cardiovascular risk prediction system using IoT-based patient monitoring and deep neural networks. The proposed system collects physiological parameters such as heart rate, blood pressure, body temperature, and ECG signals through wearable sensors. These data are transmitted to a cloud platform for storage and processing.
A deep learning model is applied to analyze the collected data and identify patterns associated with cardiovascular risks. The system classifies patient conditions into normal and risk categories and generates alerts when abnormal conditions are detected. This enables remote monitoring by healthcare professionals and supports timely medical intervention.
The proposed approach improves healthcare efficiency by enabling continuous monitoring, early diagnosis, and reduced hospital dependency. Experimental results show high accuracy and reliability, making the system suitable for real-time healthcare applications.
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