Abstract
Cardiovascular diseases (CVDs) represent serious threats to human health, causing considerable problems for the healthcare ecosystem. Medical Decision Support Systems (MDSS) have emerged as important instruments against various illnesses. However, Intelligent MDSS has substantial obstacles in interpreting complex medical data, uncertainty in noisy and imprecise data, overfitting, and the necessity for lightweight solutions. This comprehensive review study offers a thoughtful approach strategy for improving the effectiveness, the interpretability and the portability of MDSS for CVD. It combines previous studies and systems, highlighting their advantages. A speculative proposal for a new MDSS is discussed based on this study. The proposed a thoughtful approach merges the Internet of Medical Things (IoMT), Artificial Intelligence (AI), Cloud Computing, and Fuzzy Logic. Whereas the scope of this assessment does not pass for a detailed design, the suggested system has the potential to enhance patient care and outcomes in CVD.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Amirkhani, A., Papageorgiou, E.I., Mohseni, A., Mosavi, M.R.: A review of fuzzy cognitive maps in medicine: taxonomy, methods, and applications. Comput. Meth. Programs Biomed. 142, 129–145 (2017). https://doi.org/10.1016/j.cmpb.2017.02.021, https://www.sciencedirect.com/science/article/pii/S0169260716307246
Centers for Disease Control and Prevention, National Center for Health Statistics: About multiple cause of death, 1999–2020. CDC WONDER Online Database website (2022). Accessed 21 Feb 2022
Hasanova, H., Tufail, M., Baek, U.J., Park, J.T., Kim, M.S.: A novel blockchainenabled heart disease prediction mechanism using machine learning. Comput. Electr. Eng. 101(108086), 108086 (2022)
Lin, J., Fu, R., Zhong, X., Yu, P., Tan, G., Li, W., Zhang, H., Wearable sensors and devices for realtime cardiovascular disease monitoring. Cell Reports Physical Science 2(8), 100541 (2021). https://doi.org/10.1016/j.xcrp.2021.100541. https://www.sciencedirect.com/science/article/pii/S2666386421002526
Matias, I., et al.: Prediction of atrial fibrillation using artificial intelligence on electrocardiograms: a systematic review. Comput. Sci. Rev. 39(100334), 100334 (2021)
Miyachi, Y., Ishii, O., Torigoe, K.: Design, implementation, and evaluation of the computer-aided clinical decision support system based on learning-to-rank: collaboration between physicians and machine learning in the differential diagnosis process. BMC Med. Inform. Decis. Mak. 23(1), 26 (2023)
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., Raad, A.: Smart wearables for the detection of cardiovascular diseases: a systematic literature review. Sensors 23(2), 828 (2023). https://doi.org/10.3390/s23020828. https://www.mdpi.com/1424-8220/23/2/828, number: 2 Publisher: Multidisciplinary Digital Publishing Institute
Satpathy, S., Mohan, P., Das, S., Debbarma, S.: A new healthcare diagnosis system using an IoT-based fuzzy classifier with FPGA. J. Supercomput. 76(8), 5849–5861 (2020)
Stepanyan, I.V., Alimbayev, C.A., Savkin, M.O., Lyu, D., Zidun, M.: Comparative analysis of machine learning methods for prediction of heart diseases. J. Mach. Manuf. Reliab. 51(8), 789–799 (2022)
Vincent Paul, S.M., Balasubramaniam, S., Panchatcharam, P., Malarvizhi Kumar, P., Mubarakali, A.: Intelligent framework for prediction of heart disease using deep learning. Arab. J. Sci. Eng. 47(2), 2159–2169 (2022)
who.int: Cardiovasculardiseases. https://www.who.int/health-topics/cardiovascular-diseasestab=tab1.Accessed 30 March 2023
Zhen, P., Han, Y., Dong, A., Yu, J.: CareEdge: a lightweight edge intelligence framework for ECG-based heartbeat detection. Procedia Comput. Sci. 187, 329–334 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Menaceur, N.E., Kouah, S., Derdour, M. (2023). IoT-Based Intelligent Medical Decision Support System for Cardiovascular Diseases. In: Kabassi, K., Mylonas, P., Caro, J. (eds) Novel & Intelligent Digital Systems: Proceedings of the 3rd International Conference (NiDS 2023). NiDS 2023. Lecture Notes in Networks and Systems, vol 784. Springer, Cham. https://doi.org/10.1007/978-3-031-44146-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-44146-2_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-44145-5
Online ISBN: 978-3-031-44146-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)