Fog-Enabled IoT Framework for Heart Disease Diagnosis Systems


  • Quang Tran Minh 1)Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet, District 10, Ho Chi Minh City, Vietnam 2)Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu DucDistrict, Ho Chi Minh City, Vietnam
  • Do Thanh Thai 1) Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet, District 10, Ho Chi Minh City, Vietnam 2)Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu DucDistrict, Ho Chi Minh City, Vietnam
  • Phu H. Phung Department of Computer Science, University of Dayton, Dayton, OH 45469, U.S.A.
  • Phat Nguyen Huu School of Electrical and Electronics Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam



IoT, Heart Disease Diagnosis, , Fog computing, Data mining


IoT technology has been recently adopted in healthcare systems to quickly detect abnormalities from patients, diagnose diseases and provide supports in time, even remotely. In the field of heart disease, timely diagnosis and prediction help to save people. This paper proposes a fog-based IoT approach to collect and analyze electrocardiogram (ECG) signals from patients to detect abnormalities or heart attacks with a short response time so that appropriate treatments can be provided. Commonly, ECG signals are transmitted to an eco-expert system deployed on the cloud to perform preliminary automatic diagnosis using a knowledge base built from medical experts. Although such an eco-expert system assists patients and supports physicians in performing treatment for their patients, there are several open technical challenges. First, noise in raw ECG signals makes the data imprecise and reduces the prediction accuracy. Second, involving data mining and machine learning on the cloud poses a significant latency since a huge amount of data needs to be transferred in the network. This paper proposes a novel framework that can provide the integrity of the ECG data by removing noise and then extract relevant knowledge for heart disease diagnosis at the network edge based on data mining techniques. Practical experiments demonstrate that the proposed framework not only guarantees the integrity of the data but also enhances the accuracy of the real-time detection compared with previous works.


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Author Biographies

Quang Tran Minh, 1)Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet, District 10, Ho Chi Minh City, Vietnam 2)Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu DucDistrict, Ho Chi Minh City, Vietnam

Quang Tran Minh ( is an associate professor at Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Vietnam and a visiting researcher at Shibaura Institute of Technology, Tokyo, Japan. He has been a researcher at Network Design Department, KDDI Research Inc., and a researcher at Principles of Informatics Research Division, National Institute of Informatics (NII), Japan. His research interests include mobile and ubiquitous computing, IoT, network design and traffic analysis, disaster recovery systems, data mining, and ITS systems. Prof. Quang received his Ph.D. in Functional Control Systems from Shibaura Institute of Technology. He is a member of IEEE, ACM.

Do Thanh Thai, 1) Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet, District 10, Ho Chi Minh City, Vietnam 2)Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu DucDistrict, Ho Chi Minh City, Vietnam

Do Thanh Thai received his B.S and M.S degrees from Ho Chi Minh City University of Technology, VNU-HCM, Vietnam, in 2014 and 2018 respectively. His research interests include Social Network for Healthcare Services based on IoT Platform. His research works have been published on South East Asian Technical University Consortium Symposium (SEATUC) Sysposium and The Modern Artificial Intelligence and Cognitive Science Conference (MAICS).

Phu H. Phung, Department of Computer Science, University of Dayton, Dayton, OH 45469, U.S.A.

Phu H. Phung received his Ph.D. degree in computer science from Chalmers University of Technology, Sweden in 2011. He is currently an Associate professor of computer science and director of the Intelligent Systems Security Lab at the University of Dayton. His research directions focus on security solutions for intelligent systems on the web, mobile, and IoT platforms. He is also interested in malicious software detection. He is a senior member of the IEEE.

Phat Nguyen Huu, School of Electrical and Electronics Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam

Phat Nguyen Huu received his B.E. (2003), M.S. (2005) degrees in Electronics and Telecommunications at Hanoi University of Sceience and Technology (HUST), Vietnam, and Ph.D. degree (2012) in Computer Science at Shibaura Institute of Technology, Japan. Currently, he lecturer at School of Electronics and Telecommunications, HUST Vietnam. His research interests include digital image and video processing, wireless networks, ad hoc and sensor network, and intelligent traffic system (ITS) and internet of things (IoT). He received the best conference paper award in SoftCOM (2011), best student grant award in APNOMS (2011), hisayoshi yanai honorary award by Shibaura Institute of Technology, Japan in 2012.


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