Publication Type:

Conference Paper

Source:

2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, Institute of Electrical and Electronics Engineers Inc., Volume 2017-January, p.1588-1593 (2017)

ISBN:

9781509063673

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042670065&doi=10.1109%2fICACCI.2017.8126068&partnerID=40&md5=4e185d501ce7a7861041450bc34f4dd2

Keywords:

accuracy, Bagging, Behavioral research, Blood pressure, Classification algorithm, Computer aided diagnosis, decision making, Decision trees, Diagnosis, Diseases, Healthcare monitoring, Internet of things, K-nearest neighbors, Medical applications, Monitoring, Nearest neighbor search, Patient monitoring, Patient treatment, Pulse rate, Random forests, Remote patient monitoring

Abstract:

The ubiquitous growth of Internet of Things (IoT) and its medical applications has improved the effectiveness in remote health monitoring systems of elderly people or patients who need long-term personal care. Nowadays, chronic illnesses, such as, stroke, heart disease, diabetes, cancer, chronic respiratory diseases are major causes of death, in many parts of the world. In this paper, we propose a patient monitoring system for strokeaffected people to minimize future recurrence of the same by alarming the doctor and caretaker on variation in risk factors of stroke disease. Data analytics and decision-making, based on the real-time health parameters of the patient, helps the doctor in systematic diagnosis followed by tailored restorative treatment of the disease. The proposed model uses classification algorithms for the diagnosis and prediction. The ensemble method of treebased classification-Random Forest give an accuracy of 93%. © 2017 IEEE.

Notes:

cited By 0; Conference of 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 ; Conference Date: 13 September 2017 Through 16 September 2017; Conference Code:133501

Cite this Research Publication

Ani R., Krishna S., Anju, N., Sona, A. M., and Deepa, O. S., “IoT based patient monitoring and diagnostic prediction tool using ensemble classifier”, in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017, vol. 2017-January, pp. 1588-1593.