A Novel System Design for Intravenous Infusion System Monitoring for Betterment of Health Monitoring System using ML- AI
Dinesh Kumar J.R1, Ganesh Babu. C2, Soundari. D.V3, Priyadharsini. K4, Karthi S.P5
1Dinesh Kumar J.R*, ECE, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
2Dr. C. Ganeshbabu, Professor, ECE, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India.
3Soundari D.V, ECE, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
4Priyadharsini K, ECE, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
5Karthi S P, ECE, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
Manuscript received on December 14, 2019. | Revised Manuscript received on December 23, 2019. | Manuscript published on January 10, 2020. | PP: 2649-2655 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8766019320/2020©BEIESP | DOI: 10.35940/ijitee.C8766.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the upswing of contemporary science we can monitor and regulate the saline flow rate. Scrupulous flow has to be retained so that risks of fore shortening the threshold level of patient’s heart rate, blood pressure and oxygen level in blood level. Intravenous infusion used intermittently in hospital has to be checked for is purity. For the change in threshold level of patient’s body condition, saline flow has to be adjusted. The assessments obtained from the patients is proceed to the centralizer controller which is connected to the cloud is updated periodically to avoid loss of reports. The updated data sets shared to the chemist and CPU so that flow rate of saline is controlled automatically in accordance to the data received. The machine learning based algorithm (SVM) is used to predict the more accurate changes on data which is obtained from patients so that the controller can act agilie. This work gives better results based on the accuracy level calculation and efficiency improvement in terms of more fast response.
Keywords: IVF, SVM, ML & AI.
Scope of the Article: Health Monitoring and Life Prediction of Structures