Online Real Time Fuzzy Inference System Based Human Health Monitoring and Medical Decision Making

International Journal of Computer Science and Information Security, 2017. 15(4): p. 197-204

8 Pages Posted: 29 Aug 2017 Last revised: 11 Jan 2019

See all articles by Aqeel Humadi

Aqeel Humadi

Misan University - Department of Electricity

Alaa Hamoud

University of Basrah - Department of Computer Information Systems

Abstract

The medical state of patients faced a lot of researches in the last decades to enhance the medical treatment and save the lives of patients. Based on that, the automated medical diagnosis became a necessity due to its important role in reducing clinicians’ efforts and providing fast and accurate results. Remote Patients Monitoring (RPM) devices are used for this purpose by making tests and sending the results to the professionals. The proposed system is built based on three medical indicators (blood pressure, heart rate, and body temperature).The system combined both fuzzy inference system and Arduino to collect medical data, process them and make decision. The medical data are collected through sensors related to each indicator. Arduino is used to collect the sensory data and send them to Fuzzy Inference System (FIS) in order to get the infer result information to make decision. FIS gets the medical sensory data, processes them to make the assessment and sends medical state as a numeric result to Arduino to make the decision. FIS used a knowledge base written in IF-THEN rules composed of multiple linguistic variables. The proposed system can be used to help the doctors in determining the initial medical state of patients and make the right decision.

Keywords: Remote Patients Monitoring (RPM), Medical State, Blood Pressure, Heart Rate, Body Temperature, Fuzzy Inference System (FIS)

Suggested Citation

Humadi, Aqeel and Khalaf, Alaa, Online Real Time Fuzzy Inference System Based Human Health Monitoring and Medical Decision Making. International Journal of Computer Science and Information Security, 2017. 15(4): p. 197-204, Available at SSRN: https://ssrn.com/abstract=3027091 or http://dx.doi.org/10.2139/ssrn.3027091

Aqeel Humadi

Misan University - Department of Electricity

Amarah
Iraq

Alaa Khalaf (Contact Author)

University of Basrah - Department of Computer Information Systems ( email )

Basrah
Iraq

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