IoT-Assisted cardiovascular health monitoring with Ml-Based risk analysis
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Author |
R. Uma Maheswari, B. Hema Latha, Rajitha Bodasingi, A. Chaitanya Lakshmi, Dadi Lohith Sankar and A. Harsha Vardhan Reddy
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e-ISSN |
1819-6608 |
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On Pages
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375-381
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Volume No. |
21
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Issue No. |
6
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Issue Date |
May 20, 2026
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DOI |
https://doi.org/10.59018/032646
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Keywords |
IoT, Cardiovascular disease, machine learning, real-time monitoring.
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Abstract
Cardiovascular disease is now a major issue for each and everyone. Cardiovascular disease is the biggest reason for losing lives. This shows how important it is for us to detect and predict the disease early and monitor our health continuously. So, in this paper, we mainly focus on this issue and come up with a solution, which is an IoT-based cardiovascular disease prediction using Machine Learning. This continuously monitors the condition of the heart in real-time, which includes sensors, machine learning models,a web application, and cloud computing all together. The main controller used is Raspberry Pi; it collects the body readings like blood pressure, oxygen levels, Heart rate, and the ECG signals using the sensors. Later, the data is cleaned up and given to the machine learning model, which is trained using the Cleveland heart disease dataset. This helps in classifying the person as high-risk or not with good accuracy. The IoT connectivity is added so that all the sensors' live readings are sent to the cloud platform, and can give alerts. This model is cost-effective and is used in continuous monitoring at home or in emergency cases.
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