A preliminary concept for a cloud-based stroke prediction system had been put out in this project to use machine learning methods to identify oncoming strokes. An effective machine learning strategy that was produced through a distinctive analysis among multiple machine learning algorithms should be applied for the precise detection of strokes. The performance of the suggested algorithm's stroke detection was examined using 10-fold cross-validation, which was validated using two popular open-access datasets. The ML algorithm identified a level of accuracy of 97.53%, as well as sensitivity and specificity of 97.50% and 94.94%, respectively. Additionally, a real-time patient monitoring system utilizing Arduino was created and shown, capable of sensing several real-time data such as body temperature, blood pressure, blood flow, heartbeat, and oxygen level. This allows the caregiver or doctor to monitor the stroke patient around- the-clock. Decisions may be made quickly and simply with the aid of various decision-making algorithms, and anyone can access the database in accordance with their needs. The primary benefit of our technology is that it automatically creates the necessary prescription based on a person's vital signs.
Krishnan M , Puviyarasu S , Manu Raju "Cloud Based Stroke Prediction System" Iconic Research And Engineering Journals Volume 7 Issue 4 2023 Page 252-255
Krishnan M , Puviyarasu S , Manu Raju "Cloud Based Stroke Prediction System" Iconic Research And Engineering Journals, 7(4)