Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
The Internet of Medical Things (IoMT) and Artificial Intelligence (AI) have changed the traditional healthcare scheme to an intelligent system. The data are produced continuously by millions of devices and sensors, exchanging important messages through supporting network devices that monitor and control the smart-world infrastructures. While compared with cloud computing, the data storage or computation are migrated to the network (near end users) by edge computing. Therefore, edge computing is highly required to satisfy intelligent healthcare systems' requirements. However, the confluence of IoMT and AI opens up new potential in the healthcare sector. The main objective of this paper is to create a disease detection model for heart disease utilizing AI approaches. The given model includes many phases, including data gathering, preprocessing for detection of outliers, classification of disease, and weight parameter adjustment. Initially, the Correlation Based Feature Selector (CFS) approach is used in this study to exclude outliers. Then, the research work employs a Chimp Optimization Algorithm (ChOA)-based Recurrent Neural Network (RNN) model for illness diagnosis. ChOA is to fine-tune the 'weights' parameters of the RNN model to categorize medical data better. During the testing, the given ChOA -RNN model achieved extreme accuracies of 96.16 percent in identifying heart disease. As a result, the suggested model may be used as a suitable illness analysis tool for intelligent healthcare systems.
Keywords
Artificial Intelligence, Chimp Optimization Algorithm, Correlation Based Feature Selector, Health Care System, Internet Of Medical Things, Recurrent Neural Network.
H. C. Koh and G. Tan, “Data mining applications in healthcare,” Journal of healthcare information management, 19(2), p.65, 2011.
M. Sharma, G. Singh and R. Singh, “An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders,” 2019, arXiv preprint arXiv:1901.10530.
A. A. Abdellatif, A. Mohamed, C. F. Chiasserini, M. Tlili, and A. Erbad, “Edge Computing for Smart Health: Context-Aware Approaches, Opportunities, and Challenges,” IEEE Network, vol. 33, no. 3, pp. 196–203, May 2019, doi: 10.1109/mnet.2019.1800083.
Q. Cai, H. Wang, Z. Li, and X. Liu, “A Survey on Multimodal Data-Driven Smart Healthcare Systems: Approaches and Applications,” IEEE Access, vol. 7, pp. 133583–133599, 2019, doi: 10.1109/access.2019.2941419.
F. Ali et al., “A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion,” Information Fusion, vol. 63, pp. 208–222, Nov. 2020, doi: 10.1016/j.inffus.2020.06.008.
F. Al-Turjman, M. H. Nawaz, and U. D. Ulusar, “Intelligence in the Internet of Medical Things era: A systematic review of current and future trends,” Computer Communications, vol. 150, pp. 644–660, Jan. 2020, doi: 10.1016/j.comcom.2019.12.030.
F. F. Gong, X. Z. Sun, J. Lin and X. D. Gu, "Primary exploration in establishment of China's intelligent medical treatment," (in Chinese), Mod. Hos Manag., vol. 11, no. 2, pp. 28–29, 2013.
G. Manogaran, R. Varatharajan, D. Lopez, P. M. Kumar, R. Sundarasekar, and C. Thota, “A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system,” Future Generation Computer Systems, vol. 82, pp. 375–387, May 2018, doi: 10.1016/j.future.2017.10.045.
G. S. Aujla et al., “DLRS: Deep Learning-Based Recommender System for Smart Healthcare Ecosystem,” ICC 2019 - 2019 IEEE International Conference on Communications (ICC), pp. 1–6, May 2019, doi: 10.1109/icc.2019.8761416.
P. Pandiyan, S. Saravanan, R. Kannadasan, S. Krishnaveni, M. H. Alsharif, and M.-K. Kim, “A comprehensive review of advancements in green IoT for smart grids: Paving the path to sustainability,” Energy Reports, vol. 11, pp. 5504–5531, Jun. 2024, doi: 10.1016/j.egyr.2024.05.021.
S. Banka, I. Madan and S. S. Saranya, “Smart healthcare monitoring using IoT,” International Journal of Applied Engineering Research, 13(15), pp.1984-11989, 2018.
H. Zhu et al., “Smart Healthcare in the Era of Internet-of-Things,” IEEE Consumer Electronics Magazine, vol. 8, no. 5, pp. 26–30, Sep. 2019, doi: 10.1109/mce.2019.2923929.
S. Zeadally, F. Siddiqui, Z. Baig, and A. Ibrahim, “Smart healthcare,” PSU Research Review, vol. 4, no. 2, pp. 149–168, Oct. 2019, doi: 10.1108/prr-08-2019-0027.
S. S. Ram, B. Apduhan, and N. Shiratori, “A Machine Learning Framework for Edge Computing to Improve Prediction Accuracy in Mobile Health Monitoring,” Computational Science and Its Applications – ICCSA 2019, pp. 417–431, 2019, doi: 10.1007/978-3-030-24302-9_30.
Nalin M. Rajendran, et al. "Communicable Disease Prediction Using Machine Learning and Deep Learning Algorithms." International Conference on Information, Communication and Computing Technology. Singapore: Springer Nature Singapore, 2023.
A. Chaturvedi, N. Pathak, N. Sharma, and R. Mahaveerakannan, “Deep Learning in the Context of Artificial Intelligence: Advancements and Applications,” Innovative Computing and Communications, pp. 23–44, Oct. 2024, doi: 10.1007/978-981-97-4152-6_3.
J. P. Queralta, T. N. Gia, H. Tenhunen, and T. Westerlund, “Edge-AI in LoRa-based Health Monitoring: Fall Detection System with Fog Computing and LSTM Recurrent Neural Networks,” 2019 42nd International Conference on Telecommunications and Signal Processing (TSP), pp. 601–604, Jul. 2019, doi: 10.1109/tsp.2019.8768883.
M. S. Pandianchery, V. Sowmya, E. A. Gopalakrishnan, V. Ravi, and K. P. Soman, “Centralized CNN–GRU Model by Federated Learning for COVID-19 Prediction in India,” IEEE Transactions on Computational Social Systems, vol. 11, no. 1, pp. 1362–1371, Feb. 2024, doi: 10.1109/tcss.2023.3250656.
B. M and M. R, “Enhancing Hybrid Object Identification for Instantaneous Healthcare through Lorentz Force,” 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 1365–1368, Oct. 2024, doi: 10.1109/i-smac61858.2024.10714704.
Robert M Hogarth, “Methods for aggregating opinions,” In Decision Making and Change in Human Affairs; Jungermann, H., De Zeeuw, G., Eds.; Springer: Dordrecht, The Netherlands, 1977; Volume 16.
G. Isabelle, W. Jason, B. Stephen and V. Vapnik, “Gene Selection for Cancer Classification using Support Vector Machines,” Mach. Learn. 2002, 46, 389–422.
S. Selvin, R. Vinayakumar, E. A. Gopalakrishnan, V. K. Menon, and K. P. Soman, “Stock price prediction using LSTM, RNN and CNN-sliding window model,” 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1643–1647, Sep. 2017, doi: 10.1109/icacci.2017.8126078.
M. R, S. Lohmor Choudhary, R. Sharma Dixit, S. Mylapalli, and M. S. Kumar, “Enhancing Diagnostic Accuracy and Early Detection Through the Application of Deep Learning Techniques to the Segmentation of Colon Cancer in Histopathological Images,” 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 1809–1815, Oct. 2024, doi: 10.1109/i-smac61858.2024.10714728.
A. Sowmya and A. S. Pillai, “Human Fall Detection with Wearable Sensors Using ML Algorithms,” 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), pp. 1092–1095, Oct. 2021, doi: 10.1109/icosec51865.2021.9591912.
G. Dhiman, “SSC: A hybrid nature-inspired meta-heuristic optimization algorithm for engineering applications,” Knowledge-Based Systems, vol. 222, p. 106926, Jun. 2021, doi: 10.1016/j.knosys.2021.106926.
R. Yuvarani and R. Mahaveerakannan, “Enhancing IoT Data Security with a Hybrid Cryptographic Framework,” 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 565–571, Aug. 2024, doi: 10.1109/icesc60852.2024.10690010.
E. H. Houssein, M. M. Emam, and A. A. Ali, “An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on improved chimp optimization algorithm,” Expert Systems with Applications, vol. 185, p. 115651, Dec. 2021, doi: 10.1016/j.eswa.2021.115651.
F. Aktas, C. Ceken, and Y. E. Erdemli, “IoT-Based Healthcare Framework for Biomedical Applications,” Journal of Medical and Biological Engineering, vol. 38, no. 6, pp. 966–979, Dec. 2017, doi: 10.1007/s40846-017-0349-7.
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Yejnakshari Meghana K
Department of Computer Science and Engineering, Amrita School of Computing, Coimbatore, Amrita Vishwa Vidyapeetham, India.
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Cite this article
Yejnakshari Meghana K, Yellina Sri Bhargav, Radhika N, Uma Jothi, Radhika G and Mahaveerakannan R, “Chimp Optimization Algorithm based Recurrent Neural Network for Smart Health Care System in Edge computing-based IoMT”, Journal of Machine and Computing. doi: 10.53759/7669/jmc202505034.
