Deep learning framework for analysis of health factors in Internet-of-Medical Things
DOI:
https://doi.org/10.3103/S0735272723030056Keywords:
Internet-of-Medical-Things, CNN, Health Pattern Discovery, Deep Learning, Healthcare, IoMTAbstract
The introduction of IoT technologies, such as those used in remote health monitoring applications, has revolutionized conventional medical care. Furthermore, the approach utilized to obtain insights from the scrutiny of lifestyle elements and activities is crucial to the success of tailored healthcare and disease prevention services. Intelligent data retrieval and classification algorithms allow for the investigation of disease and the prediction of aberrant health states. The Convolutional Neural Network (CNN) strategy is utilized to forecast such anomaly because it can successfully recognize the knowledge significant to disease anticipation from amorphous medical heath records. Conversely, if a fully coupled network-topology is used, CNN guzzles a huge memory. Furthermore, the complexity analysis of the model may rise as the number of layers grows. Therefore, we present a CNN target recognition and anticipation strategy based on the Pearson Correlation Coefficient (PCC) and standard pattern activities to address these shortcomings of the CNN model. It is built in this framework and used for classification purposes. In the initial hidden layer, the most crucial health-related factors are chosen, and in the next, a correlation-coefficient examination is performed to categorize the health factors into positively and negatively correlated groups. Mining the occurrence of regular patterns among the categorized health parameters also reveals the behavior of regular patterns. The model output is broken down into obesity, hypertension, and diabetes-related factors with known correlations. To lessen the impact of the CNN-typical knowledge discovery paradigm, we use two separate datasets. The experimental results reveal that the proposed model outperforms three other machine learning techniques while requiring less computational effort.
References
M. A. Almaiah, A. Ali, F. Hajjej, M. F. Pasha, M. A. Alohali, “A lightweight hybrid deep learning privacy preserving model for FC-based industrial internet of medical things,” Sensors, vol. 22, no. 6, p. 2112, 2022, doi: https://doi.org/10.3390/s22062112.
M. M. Kamruzzaman, I. Alrashdi, A. Alqazzaz, “New opportunities, challenges, and applications of edge-AI for connected healthcare in Internet of Medical Things for smart cities,” J. Healthc. Eng., vol. 2022, pp. 1–14, 2022, doi: https://doi.org/10.1155/2022/2950699.
P. Manickam et al., “Artificial Intelligence (AI) and Internet of Medical Things (IoMT) assisted biomedical systems for intelligent healthcare,” Biosensors, vol. 12, no. 8, p. 562, 2022, doi: https://doi.org/10.3390/bios12080562.
M. N. Hossen, V. Panneerselvam, D. Koundal, K. Ahmed, F. M. Bui, S. M. Ibrahim, “Federated machine learning for detection of skin diseases and enhancement of Internet of Medical Things (IoMT) security,” IEEE J. Biomed. Heal. Informatics, vol. 27, no. 2, pp. 835–841, 2023, doi: https://doi.org/10.1109/JBHI.2022.3149288.
R. Rawat, “A systematic review of blockchain technology use in e-supply chain in Internet of Medical Things (IoMT),” Int. J. Comput. Inf. Manuf., vol. 2, no. 2, 2022, doi: https://doi.org/10.54489/ijcim.v2i2.119.
R. Chaganti, A. Mourade, V. Ravi, N. Vemprala, A. Dua, B. Bhushan, “A particle swarm optimization and deep learning approach for intrusion detection system in Internet of Medical Things,” Sustainability, vol. 14, no. 19, p. 12828, 2022, doi: https://doi.org/10.3390/su141912828.
A. K. Maurya, K. Lokesh, . Sandeep, R. Kumar, S. Arun, R. Krishnamoorthy, “Deep neuro-fuzzy logic technique for brain meningiomasa prediction,” in 2022 7th International Conference on Communication and Electronics Systems (ICCES), 2022, pp. 1244–1248, doi: https://doi.org/10.1109/ICCES54183.2022.9836008.
G. N. Vivekananda, A. R. H. Ali, S. Arun, P. Mishra, R. Sengar, R. Krishnamoorthy, “Cloud based effective health care management system with Artificial Intelligence,” in 2022 IEEE 7th International conference for Convergence in Technology (I2CT), 2022, pp. 1–6, doi: https://doi.org/10.1109/I2CT54291.2022.9825457.
S. S. Band et al., “A survey on machine learning and internet of medical things-based approaches for handling COVID-19: Meta-analysis,” Front. Public Heal., vol. 10, 2022, doi: https://doi.org/10.3389/fpubh.2022.869238.
K. Lokesh, S. Srivastava, M. P. Kumar, S. Arun, S. Padmapriya, R. Krishnamoorthy, “Detection of stomach cancer using deep neural network in healthcare sector,” in 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 2021, pp. 521–526, doi: https://doi.org/10.1109/ICAC3N53548.2021.9725656.
M. K. Kagita, N. Thilakarathne, T. R. Gadekallu, P. K. R. Maddikunta, “A review on security and privacy of internet of medical things,” in Intelligent Internet of Things for Healthcare and Industry. Internet of Things, 2022, pp. 171–187.
S. A. Moqurrab et al., “A deep learning-based privacy-preserving model for smart healthcare in Internet of Medical Things using fog computing,” Wirel. Pers. Commun., vol. 126, no. 3, pp. 2379–2401, 2022, doi: https://doi.org/10.1007/s11277-021-09323-0.
S. Razdan, S. Sharma, “Internet of Medical Things (IoMT): Overview, emerging technologies, and case studies,” IETE Tech. Rev., vol. 39, no. 4, pp. 775–788, 2022, doi: https://doi.org/10.1080/02564602.2021.1927863.
A. I. Awad, M. M. Fouda, M. M. Khashaba, E. R. Mohamed, K. M. Hosny, “Utilization of mobile edge computing on the Internet of Medical Things: A survey,” ICT Express, vol. 9, no. 3, pp. 473–485, 2023, doi: https://doi.org/10.1016/j.icte.2022.05.006.
Z. Ashfaq et al., “A review of enabling technologies for Internet of Medical Things (IoMT) ecosystem,” Ain Shams Eng. J., vol. 13, no. 4, p. 101660, 2022, doi: https://doi.org/10.1016/j.asej.2021.101660.
S. Alam et al., “Blockchain-based solutions supporting reliable healthcare for fog computing and Internet of Medical Things (IoMT) integration,” Sustainability, vol. 14, no. 22, p. 15312, 2022, doi: https://doi.org/10.3390/su142215312.
J. Fan, X. Wang, Y. Guo, X. Hu, B. Hu, “Federated learning driven secure Internet of Medical Things,” IEEE Wirel. Commun., vol. 29, no. 2, pp. 68–75, 2022, doi: https://doi.org/10.1109/MWC.008.00475.
M. A. Jabbar, S. K. Shandilya, A. Kumar, S. Shandilya, “Applications of cognitive internet of medical things in modern healthcare,” Comput. Electr. Eng., vol. 102, p. 108276, 2022, doi: https://doi.org/10.1016/j.compeleceng.2022.108276.
M. K. Hasan et al., “A review on security threats, vulnerabilities, and counter measures of 5G enabled Internet‐of‐Medical‐Things,” IET Commun., vol. 16, no. 5, pp. 421–432, 2022, doi: https://doi.org/10.1049/cmu2.12301.
A. Kumar, N. Gaur, S. Chakravarti, “Improving and analysing the spectral access performance of QAM-64 optical NOMA using a hybrid ED-CSD algorithm,” J. Opt. Commun., 2023, doi: https://doi.org/10.1515/joc-2023-0217.
A. Kumar, M. A. Albreem, M. Gupta, M. H. Alsharif, S. Kim, “Future 5G network based smart hospitals: Hybrid detection technique for latency improvement,” IEEE Access, vol. 8, pp. 153240–153249, 2020, doi: https://doi.org/10.1109/ACCESS.2020.3017625.
![AI role in healthcare](https://radioelektronika.org/public/journals/16/submission_286321_324551_coverImage_en_US.png)