SpringerLink
Log in

6G Wireless with Cyber Care and Artificial Intelligence for Patient Data Prediction

  • Research
  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The Traditional hospital manual-centric focused model has been replaced with the decentralised patient-centric approach in recent data processing models. The advent of the 6G communication model makes high data rate transmission in less time for remote healthcare services. As healthcare applications grow, data are generated in numerous forms and sizes. Due to the data rate, latency, and bandwidth complexity, the next-generation communication model, 6G, will be utilised in future healthcare domains. This research uses 6G-enabled cybercare devices to monitor the patients. The data from cyber care devices are secured with a Deep learning model. Further critical data is immediately analysed using an intelligent data analytics model, and patients are saved in time. The enhanced rectified linear unit with a feed-forward deep neural network is used to analyse and predict patient data. An improved fixed linear unit with a feed-forward neural network for analysing and predicting patient data enhances the system's predictive capabilities. Furthermore,the performance of the prediction system is improved with the evolutionary approach called Artificial Bee Colony (ABC) based feature selection by selecting the most relevant features. By training the model on historical patient data, it can learn complex relationships and make accurate predictions about future health outcomes or events, enabling proactive interventions and personalised treatment plans. In addition, the developed ABC-Feed-FDNN model provides better results compared to existing methods in terms of obtained accuracy, precision, recall and F1 score.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Availability of Data and Material

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Murakami, T., Kishi, Y., Ishibashi, K., Kasai, K., Shinbo, H., Tamai, M., Tsuda, K., Nakazawa, M., Tsukamoto, Y., & Yokoyama, H.et al. (2020). Research project to realize various high-reliability communications in advanced 5G network. In: Proceedings of the 2020 IEEE wireless communications and networking conference (WCNC), Online, 25–28 May 2020; pp. 1–8.

  2. Suyama, S., Okayama, T., Kishiyama, Y., Nagata, S., & Takahiro, A. (2021). A study on extreme wideband 6G radio access technologies for achieving 100Gbps data rate in higher frequency bands. IEICE Transactions on Communications, E104.B, pp. 992–999.

  3. Li, Q., You, T., Chen, J., Zhang, Y., & Du, C, (2023). LI-EMRSQL: Linking Information Enhanced Text2SQL Parsing on Complex Electronic Medical Records. IEEE Transactions on Reliability.

  4. Noh, S., Lee, J., Lee, G., Seo, K., Sung, Y., & Yu, H. (2022). Channel estimation techniques for ris-assisted communication: millimeter wave and Sub-THz systems. IEEE Vehicular Technology Magazine, 17, 64–73.

    Article  Google Scholar 

  5. Liu, D., Liu, X., Chen, Z., Zuo, Z., Tang, X., Huang, Q., & Arai, T, (2022). Magnetically driven soft continuum microrobot for intravascular operations in microscale. Cyborg and Bionic Systems.

  6. Zhang, J., Fang, Q., **ang, P., Sun, D., Xue, Y., **, R., & Lu, H, (2022). A survey on design, actuation, modeling, and control of continuum robot. Cyborg and Bionic Systems.

  7. Sampathila, N., Chadaga, K., Goswami, N., Chadaga, R. P., Pandya, M., Prabhu, S., Bairy, M. G., Katta, S. S., Bhat, D., & Upadya, S. P. (1812). Customized deep learning classifier for detection of acute lymphoblastic leukemia using blood smear images. Healthcare, 2022, 10.

    Google Scholar 

  8. Maritta, A. V., Tella, L., Kirsi, H., Jaakko, V., Gaoming, L., Yao, T., & **anhong, L. (2021). Measured and perceived impacts of evidence-based leadership in nursing: a mixed-methods systematic review protocol. British Medical Journal Open, 11(10), 2021.

    Google Scholar 

  9. Ding, X., Wang, L., Sun, J., Li, D., Zheng, B., He, S., & Latour, J. M, (2020). Effectiveness of empathy clinical education for children's nursing students: A quasi-experimental study. Nurse Education Today, 85.

  10. Hu, S., Chen, W., Hu, H., Huang, W., Chen, J., & Hu, J. (2022). Coaching to develop leadership for healthcare managers: a mixed-method systematic review protocol. Systematic Reviews, 11(1), 67.

    Article  Google Scholar 

  11. AbdElaziz, M., Mabrouk, A., Dahou, A., & Chelloug, S. A. (2022). Medical image classification utilizing ensemble learning and levy flight-based honey badger algorithm on 6g-enabled internet of things. Computational Intelligence and Neuroscience, 2022, 5830766.

    Google Scholar 

  12. Cao, K., Wang, B., Ding, H., Lv, L., Tian, J., Hu, H., & Gong, F. (2021). Achieving reliable and secure communications in wireless-powered NOMA systems. IEEE Transactions on Vehicular Technology, 70(2), 1978–1983.

    Article  Google Scholar 

  13. Zou, X., Yuan, J., Shilane, P., **a, W., Zhang, H., & Wang, X. (2022). From hyper-dimensional structures to linear structures: Maintaining deduplicated data’s locality. ACM Transactions on Storage, 18(3), 1–28.

    Article  Google Scholar 

  14. **a, W., Pu, L., Zou, X., Shilane, P., Li, S., Zhang, H., & Wang, X. (2023). The design of fast and lightweight resemblance detection for efficient post-deduplication delta compression. ACM Transactions on Storage, 19(3), 1–30.

    Article  Google Scholar 

  15. Yang, H., & Li, Z. (2024). Dynamic graph convolutional network-based prediction of the urban grid-level taxi demand-supply imbalance using GPS trajectories. ISPRS International Journal of Geo-Information, 13(2), 34.

    Article  Google Scholar 

  16. Nasser, N., Emad-Ul-Haq, Q., Imran, M., Ali, A., Razzak, I., & Al-Helali, A. (2023). A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing. Neural Computing and Applications, 35(19):13775–13789. https://doi.org/10.1007/s00521-021-06396-7. Epub 2021 Sep 10. PMID: 34522068; PMCID: PMC8431959.

  17. Liu, H., Zhang, S., Gamboa, H., Xue, T., Zhou, C., & Schultz, T, (2024). Taxonomy and real-time classification of artifacts during biosignal acquisition: a starter study and dataset of ECG. IEEE Sensors Journal.

  18. Nasralla, M. M., Khattak, S. B. A., Ur Rehman, I., & Iqbal, M. (2023). Exploring the role of 6G technology in enhancing quality of experience for m-health multimedia applications: A comprehensive survey. Sensors, 23, 5882. https://doi.org/10.3390/s23135882

    Article  Google Scholar 

  19. **ao, Z., Fang, H., Jiang, H., Bai, J., Havyarimana, V., Chen, H., & Jiao, L. (2023). Understanding private car aggregation effect via spatio-temporal analysis of trajectory data. IEEE Transactions on Cybernetics, 53(4), 2346–2357.

    Article  Google Scholar 

  20. **ao, Z., Li, H., Jiang, H., Li, Y., Alazab, M., Zhu, Y., & Dustdar, S. (2023). Predicting urban region heat via learning arrive-stay-leave behaviors of private cars. IEEE Transactions on Intelligent Transportation Systems, 24(10), 10843–10856.

    Article  Google Scholar 

  21. Karaboga, D. (2005). An idea on honey bee swarm for numerical optimization. Erciyes University.

    Google Scholar 

  22. He, B., Zhang, Y., Zhou, Z., Wang, B., Liang, Y., Lang, J., & Tian, G. (2020). A neural network framework for predicting the tissue-of-origin of 15 common cancer types based on RNA-Seq data. Frontiers in Bioengineering and Biotechnology, 8, 21.

    Article  Google Scholar 

  23. El-Shafeiy, E., Sallam, K. M., Chakrabortty, R. K., & Abohany, A. A. (2021). A clustering based Swarm Intelligence optimization technique for the Internet of Medical Things. Expert Systems with Applications, 173, 114648.

    Article  Google Scholar 

  24. Hu, J., Wu, Y., Li, T., & Ghosh, B. K. (2019). Consensus control of general linear multiagent systems with antagonistic interactions and communication noises. IEEE Transactions on Automatic Control, 64(5), 2122–2127.

    Article  MathSciNet  Google Scholar 

  25. Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 376, 113609.

    Article  MathSciNet  Google Scholar 

  26. Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249.

  27. Chen, B., Hu, J., Zhao, Y., & Ghosh, B. (2022). K, Finite-time velocity-free rendezvous control of multiple AUV systems with intermittent communication. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(10), 6618–6629.

    Article  Google Scholar 

  28. Habibzadeh, M., Krzy˙ zak, A., Fevens, T. (2013). White blood cell differential counts using convolutional neural networks for low resolution images. In: proceedings of the international conference on artificial intelligence and soft computing, Zakopane, Poland, June, 9–13 2013; pp. 263–274.

  29. Wang, Q., Hu, J., Wu, Y., & Zhao, Y. (2023). Output synchronization of wide-area heterogeneous multi-agent systems over intermittent clustered networks. Information Sciences, 619, 263–275.

    Article  Google Scholar 

  30. Sharma, M., Bhave, A., & Janghel, R. R. (2019). White blood cell classification using convolutional neural network. In: Soft computing and signal processing. Springer: Berlin/Heidelberg, Germany, pp. 135–143.

  31. Jiang, Y., & Li, X., (2022). Broadband cancellation method in an adaptive co-site interference cancellation system. International Journal of Electronics, 109(5), 854–874.

  32. Zhao, L., Qu, S., Xu, H., Wei, Z., & Zhang, C. (2024). Energy-efficient trajectory design for secure SWIPT systems assisted by UAV-IRS. Vehicular Communications, 45(100725), 2024.

    Google Scholar 

  33. Zhang, X., Deng, H., **ong, Z., Liu, Y., Rao, Y., Lyu, Y., & Li, Y, (2024). Secure routing strategy based on attribute-based trust access control in social-aware networks. Journal of Signal Processing Systems.

  34. Lyu, T., Xu, H., Zhang, L., & Han, Z. (2024). Source selection and resource allocation in wireless-powered relay networks: An adaptive dynamic programming-based approach. IEEE Internet of Things Journal, 11(5), 8973–8988.

    Article  Google Scholar 

  35. Xu, H., Han, S., Li, X., & Han, Z. (2023). Anomaly traffic detection based on communication-efficient federated learning in space-air-ground integration network. IEEE Transactions on Wireless Communications, 22(12), 9346–9360.

    Article  Google Scholar 

  36. Liu, G. (2021). Data collection in mi-assisted wireless powered underground sensor networks: Directions, recent advances, and challenges. IEEE Communications Magazine, 59(4), 132–138.

    Article  Google Scholar 

  37. Wu, Z., Zhu, H., He, L., Zhao, Q., Shi, J., & Wu, W. (2023). Real-time stereo matching with high accuracy via spatial attention-guided upsampling. Applied Intelligence, 53(20), 24253–24274.

    Article  Google Scholar 

  38. Wu, W., Zhu, H., Yu, S., & Shi, J. (2019). Stereo matching with fusing adaptive support weights. IEEE Access, 7(61960–61974), 2019.

    Google Scholar 

  39. Hou, M., Zhao, Y., & Ge, X. (2017). Optimal scheduling of the plug-in electric vehicles aggregator energy and regulation services based on grid to vehicle. International Transactions on Electrical Energy Systems, 27(6), e2364.

    Article  Google Scholar 

  40. Zhang, J., Zhu, D., Jian, W., Hu, W., Peng, G., Chen, Y., & Wang, Z., (2024). fractional order complementary non-singular terminal sliding mode control of PMSM based on neural network. International Journal of Automotive Technology.

  41. Lu, C., Liu, Q., Zhang, B., & Yin, L, (2022). A Pareto-based hybrid iterated greedy algorithm for energy-efficient scheduling of distributed hybrid flowshop. Expert Systems with Applications, 204.

  42. Lu, C., Gao, R., Yin, L., & Zhang, B. (2024). Human-robot collaborative scheduling in energy-efficient welding shop. IEEE Transactions on Industrial Informatics, 20(1), 963–971.

    Article  Google Scholar 

  43. Mou, J., Gao, K., Duan, P., Li, J., Garg, A., & Sharma, R. (2023). A machine learning approach for energy-efficient intelligent transportation scheduling problem in a real-world dynamic circumstances. IEEE Transactions on Intelligent Transportation Systems, 24(12), 15527–15539.

    Article  Google Scholar 

  44. Xu, Y., Wang, E., Yang, Y., & **ong, H. (2024). GS-RS: A generative approach for alleviating cold start and filter bubbles in recommender systems. IEEE Transactions on Knowledge and Data Engineering, 36(2), 668–681.

    Google Scholar 

  45. Liu, Y., Fang, Z., Cheung, M. H., Cai, W., & Huang, J. (2023). Mechanism design for blockchain storage sustainability. IEEE Communications Magazine, 61(8), 102–107.

    Article  Google Scholar 

  46. Cao, K., Ding, H., Li, W., Lv, L., Gao, M., Gong, F., Wang, B., (2022.). On the ergodic secrecy capacity of intelligent reflecting surface aided wireless powered communication systems. IEEE Wireless Communications Letters, pp. 1.

  47. Cheng, B., Wang, M., Zhao, S., Zhai, Z., Zhu, D., & Chen, J. (2017). Situation-aware dynamic service coordination in an IoT environment. IEEE/ACM Transactions on Networking, 25(4), 2082–2095.

    Article  Google Scholar 

  48. Lu, S., Yang, J., Yang, B., Li, X., Yin, Z., Yin, L., & Zheng, W, (v). Surgical instrument posture estimation and tracking based on LSTM. ICT Express.

  49. Zheng, W., Lu, S., Yang, Y., Yin, Z., Yin, L., &. Ali, H., (2024). Lightweight transformer image feature extraction network. PeerJ Computer Science, 10, e1755.

  50. Cao, B., Zhao, J., Yang, P., Gu, Y., Muhammad, K., Rodrigues, J. J. P. C., & de Albuquerque, V. H. C, (2020). Multiobjective 3-D topology optimization of next-generation wireless data center network. IEEE Transactions on Industrial Informatics, 16(5), 3597–3605.

  51. Liu, X., Zhao, J., Li, J., Cao, B., & Lv, Z, (2022). Federated neural architecture search for medical data security. IEEE Transactions on Industrial Informatics, 18(8), 5628–5636.

Download references

Acknowledgements

Abdullah Alshammari would like to express sincere gratitude to University of Hafr Albatin, Hafar Albatin, Saudi Arabia, for support. The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA, for funding this research work through the project number NBU-FFR-2024-1563-04. Nisreen Innab would like to express sincere gratitude to AlMaarefa University, Riyadh, Saudi Arabia, for providing funding to conduct this research.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, University of Hafr Albatin, 31991, Hafar Albatin, Saudi Arabia

    Abdullah Alshammari

  2. Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, 13713, Diriyah, Riyadh, Saudi Arabia

    Nisreen Innab

  3. Department of Electrical Engineering, College of Engineering, Northern Border University, Arar, Saudi Arabia

    Hafedh Mahmoud Zayani

  4. Department of Mathematics, College of Science and Arts, King Abdul Aziz University, Rabigh, Saudi Arabia

    Meshal Shutaywi & Wejdan Deebani

  5. Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, 21944, Taif, Saudi Arabia

    Roobaea Alroobaea

  6. Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, 11952, Majmaah, Saudi Arabia

    Laila Almutairi

Authors
  1. Abdullah Alshammari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nisreen Innab
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hafedh Mahmoud Zayani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Meshal Shutaywi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Roobaea Alroobaea
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wejdan Deebani
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Laila Almutairi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AA: Conceptualization, Methodology, Formal analysis, Validation, Resources, Supervision, Writing—original draft, Writing—review and editing. NI: Validation, Resources, Supervision, Writing—original draft, Writing—review and editing. HMZ: Formal analysis, Validation, Writing—original draft, Writing—review and editing. MS: Formal analysis, Validation, Writing—original draft, Writing—review and editing. RA: Validation, Writing—original draft, Writing—review and editing. WD: Validation, Writing—original draft, Writing—review and editing. LA: Validation, Writing—original draft, Writing—review and editing.

Corresponding author

Correspondence to Nisreen Innab.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethics Approval and Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alshammari, A., Innab, N., Zayani, H.M. et al. 6G Wireless with Cyber Care and Artificial Intelligence for Patient Data Prediction. Wireless Pers Commun (2024). https://doi.org/10.1007/s11277-024-11024-3

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11277-024-11024-3

Keywords

Search

Navigation