A new approach to IoT-based disease diagnosis using genetic algorithms and various classifiers
Subject Areas : Special
seyed ebrahim dashti
1
,
maryam nikpor
2
,
mehdi nikpor
3
,
mahbobe johari
4
1 - Assistant Professor, Faculty of Electrical and Computer Engineering, Jahrom Branch, Islamic Azad University, Jahrom, Fars, Iran
2 - PhD student, Faculty of Electrical and Computer Engineering, Islamic Azad University, Fars, Iran
3 - Instructor, Bandar Abbas University of Medical Sciences, Hormozgan, Iran
4 - Master's degree, Fasa Islamic Azad University, Fars, Iran
Keywords: Smart health, machine learning, Internet of Things (IoT), Ensemble learning, diabetes,
Abstract :
Medical information technology and health services are related to the national welfare and livelihood of the people. The integration of cloud computing and the Internet of Things will be a major breakthrough in modern medical applications. This study focuses on the chronic disease of diabetes, which is one of the leading causes of death worldwide. This research has applied medical information technology in the field of IoT, especially in the field of medical monitoring and management applications. A model architecture for remote monitoring and management of the health information cloud platform is proposed and analyzed, and then an algorithm based on genetic algorithm and hybrid classification for the diagnosis of diabetes is proposed for medical monitoring. The results show that the proposed method has a higher performance than the basic methods and has reached an accuracy of 94%.
[1] Ahmad, M.O., Siddiqui, S.T. (2022). The Internet of Things for Healthcare: Benefits, Applications, Challenges, Use Cases and Future Directions. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Mishra, K., Singh, B.K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 318. .
[2] Hameed, G., Singh, Y., Haq, S., Rana, B. (2022). Blockchain-Based Model for Secure IoT Communication in Smart Healthcare. In: Singh, P.K., Kolekar, M.H., Tanwar, S., Wierzchoń, S.T., Bhatnagar, R.K. (eds) Emerging Technologies for Computing, Communication and Smart Cities. Lecture Notes in Electrical Engineering, vol 875..
[3] Li, W., Chai, Y., Khan, F. et al. A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System. Mobile Netw Appl 26, 234–252 (2021)..
[4] Zheng, T., Xie, W., Xu, L., He, X., Zhang, Y., You, M., ... & Chen, Y. (2017). A machine learning-based framework to identify type 2 diabetes through electronic health records. International journal of medical informatics, 97, 120-127.
[5] Somasundaram, S. K., & Alli, P. (2017). A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy. Journal of Medical Systems, 41(12), 201.
[6] Nilashi, M., bin Ibrahim, O., Ahmadi, H., & Shahmoradi, L. (2017). An Analytical Method for Diseases Prediction Using Machine Learning Techniques. Computers & Chemical Engineering.
[7] Nilashi, M., Bin Ibrahim, O., Mardani, A., Ahani, A., & Jusoh, A. (2016). A soft computing approach for diabetes disease classification. Health Informatics Journal, 1460458216675500.
[8] Pouriyeh, S., Vahid, S., Sannino, G., De Pietro, G., Arabnia, H., & Gutierrez, J. (2017, July). A comprehensive investigation and comparison of Machine Learning Techniques in
the domain of heart disease. In Computers and Communications (ISCC), 2017 IEEE Symposium on (pp. 204-207). IEEE.
[9] Zheng, T., Xie, W., Xu, L., He, X., Zhang, Y., You, M., ... & Chen, Y. (2017). A machine learning-based framework to identify type 2 diabetes through electronic health records. International journal of medical informatics, 97, 120-127.
[10] Yaqoob I (2021) Blockchain for healthcare data management: opportunities, challenges, and future recommendations. Neural Comput Appl 0123456789.
[11] Shaikh, F. K., Zeadally, S., & Exposito, E. (2017). Enabling technologies for green internet of things. IEEE Systems Journal, 11(2), 983-994.
[12] Sicari, S., Rizzardi, A., Grieco, L. A., Piro, G., & Coen-Porisini, A. (2017). A Policy Enforcement Framework for Internet of Things Applications in the Smart Health. Smart Health.
[13] Velliangiri S, Karthikeyan Karunya P (2020) Blockchain technology: challenges and security issues in consensus algorithm. In: International conference on computer communication and informatics, ICCCI.
[14] Hossain, M. S., & Muhammad, G. (2016). Cloud-assisted industrial internet of things (iiot)–enabled framework for health monitoring. Computer Networks, 101, 192-202.
[15] Temko, A. (2017). Accurate wearable heart rate monitoring during physical exercises using PPG. IEEE Transactions on Biomedical Engineering.
[16] Philip NY, Rodrigues JJPC, Wang H, Fong SJ, Chen J (2021) Internet of Things for in-home health monitoring systems: current advances, challenges and future directions. IEEE J Sel Areas Commun 39(2):300–310.
