Abstract
The Internet of Things (IoT) is developing a more significant transformation in the healthcare industry by improving patient care with reduced cost of treatments. Main aim of this research is to monitor the Covid-19 patients and report the health issues immediately using IoT. Collected data is analyzed using deep learning model. The technological advancement of sensor and mobile technologies came up with IoT-based healthcare systems. These systems are more preventive than the traditional healthcare systems. This paper developed an efficient real-time IoT-based COVID-19 monitoring and prediction system using a deep learning model. By collecting symptomatic patient data and analyzing it, the COVID-19 suspects are predicted in the early stages in a better way. The effective parameters are selected using the Modified Chicken Swarm optimization (MCSO) approach by mining the health parameters gathered from the sensors. The COVID-19 presence is computed using the hybrid Deep learning model called Convolution and graph LSTM using the desired features. (ConvGLSTM). This process includes four stages such as data collection, data analysis (feature selection), diagnostic system (DL model), and the cloud system (Storage). The developed model is experimented with using the dataset from Srinagar based on parameters such as accuracy, precision, recall, F1 score, RMSE, and AUC. Based on the outcome, the proposed model is effective and superior to the traditional approaches to the early identification of COVID-19.
Similar content being viewed by others
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
References
Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), World Health Organization, 2020. Accessed 21 July, https://coronavirus.jhu.edu/map.html
WHO Director-General’s Opening Remarks at the media Briefing on COVID-19 -. 11 March, 2020. Accessed 11 March 2020, https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19-.11-march-2020
New York Post, The Most Promising Coronavirus Breakthroughs so Far, from Vaccines to Treatments, 2020. April 8, https://nypost.com/2020/04/08/coronavirus-breakthroughs-how-close-are-we-to-a-vaccine/
Hlaing, P.M., Nopparatjamjomras, T.R., Nopparatjamjomras, S.: Digital technology for preventative health care in Myanmar. Digit. Med. 4(3), 117–121 (2018). https://doi.org/10.4103/digm.digm_25_18
Cerina, L., Notargiacomo, S., Paccanit, M.G., Santambrogio, M.D.: A fog-computing architecture for preventive healthcare and assisted living in smart ambients. IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), pp 1–6 (2017). https://doi.org/10.1109/RTSI.2017.8065939. Modena
Dinesen, B., Nonnecke, B., Lindeman, D., et al.: Personalized telehealth in the future: a global research agenda. J. Med. Internet Res. 18(3), e53 (2016). https://doi.org/10.2196/jmir.5257
Li, T., Xia, T., Wang, H., Tu, Z., Tarkoma, S., Han, Z.,… Hui, P.: Smartphone app usage analysis: datasets, methods, and applications. IEEE Commun. Surv. Tutor. 24(2), 937–966 (2022)
Srinivasa Rao, A.S.R., Vazquez, J.A.: Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey when cities and towns are under quarantine. Infect. Control Hosp. Epidemiol. 41(7), 826–830 (2020)
Zou, Y., Zhong, M., Li, S., Qing, Z., Xing, X., Gong, G., …, Zhou, C.: Flexible wearable strain sensors based on laser-induced graphene for monitoring human physiological signals. Polymers. 15(17) (2023)
Fu, C., Yuan, H., Xu, H., Zhang, H., Shen, L.: TMSO-Net: texture adaptive multi-scale observation for light field image depth estimation. J Vis. Commun. Image Represent. 90, 103731 (2023)
Kumar, K., Kumar, N., Shah, R.: Role of IoT to avoid spreading of COVID-19. Int. J. Intell. Netw. 1, 32–35 (2020)
Iqbal, U., Mir, A.H.: Efficient and dynamic access control mechanism for secure data acquisition in IoT environment. Int. J. Comput. Digit. Syst. 10(1), 9–28 (2021)
Zhang, J., Shen, Q., Ma, Y., et al.: Calcium homeostasis in Parkinson’s disease: from pathology to treatment. Neurosci. Bull. 38, 1267–1270 (2022)
Zhu, Y., Huang, R., Wu, Z., Song, S., Cheng, L., …, Zhu, R.: Deep learning-based predictive identification of neural stem cell differentiation. Nat. Commun. 12(1), 2614 (2021)
Cao, K., Wang, B., Ding, H., Lv, L., Tian, J., Hu, H.,… Gong, F.: Achieving reliable and secure communications in wireless-powered NOMA Systems. IEEE Trans. Veh. Technol. 70(2), 1978–1983 (2021)
Liu, N., Liang, G., Li, L., Zhou, H., Zhang, L., … Song, X.: An eyelid parameters auto-measuring method based on 3D scanning. Displays 69, 102063 (2021)
Jiang, H., Wang, M., Zhao, P., Xiao, Z., Dustdar, S.: A utility-aware general framework with quantifiable privacy preservation for destination prediction in LBSs. IEEE/ACM Trans. Netw. 29(5), 2228–2241 (2021)
Tuli, S., Tuli, S., Tuli, R., Singh, S.: Since January 2020 Elsevier Has Created a COVID-19 Resource centre with Free Information in English and Mandarin on the Novel Coronavirus COVID- 19. .e COVID-19 Resource centre Is Hosted, Elsevier Connect, the Company’ s Public News and Information, Amsterdam, Netherlands (2020)
Mir, M.H., Jamwal, S., Mehbodniya, A., Garg, T., Iqbal, U., Samori, I.A.: IoT-enabled framework for early detection and prediction of COVID-19 suspects by leveraging machine learning in cloud. Hindawi J. Healthc. Eng. 2022, Article ID 7713939, 16 pages. https://doi.org/10.1155/2022/7713939
Polsinelli, M., Cinque, L., Placidi, G.: A light CNN for detecting COVID-19 from CT scans of the chest. Pattern Recogn Lett 140, 95–100 (2020)
Li, C., Dong, M., Xin, X., Li, J., Chen, X., …, Ota, K.: Efficient privacy preserving in IoMT with blockchain and lightweight secret sharing. IEEE Internet Things J. 10(24), 22051–22064 (2023)
Dai, X., Xiao, Z., Jiang, H., Alazab, M., Lui, J.C.S., Dustdar, S., …, Liu, J.: Task co-offloading for D2D-assisted mobile edge computing in industrial internet of things. IEEE Trans. Industr. Inform. 19(1), 480–490 (2023)
Han, S., Ding, H., Zhao, S., Ren, S., Wang, Z., Lin, J.,… Zhou, S.: Practical and robust federated learning with highly scalable regression training. IEEE Trans. Neural Netw. Learn. Syst. (2023)
Ghoshal, B., Tucker, A.: Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. (2020). arXiv preprint arXiv:2003.10769
Brunese, L., Mercaldo, F., Reginelli, A., Santone, A.: Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays. Comput. Methods Programs Biomed. 196, 105608 (2020)
Ardakani, A.A., Kanafi, A.R., Acharya, U.R., Khadem, N., Mohammadi, A.: Application of deep learning technique to manage COVID19 in routine clinical practice using CT images: results of 10 convolutional neural networks. Comput. Biol. Med. 121, 103795 (2020). https://doi.org/10.1016/j.compbiomed.2020.103795
Aswathy, S.U., Jarin, T., Mathews, R., Nair, L.M., Rroan, M.: CAD systems for automatic detection and classification of COVID-19 in nano CT lung image by using machine learning technique. Int. J. Pharmacol. Res. 12, 1865–1870 (2020)
Ding, Y., Zhang, W., Zhou, X., Liao, Q., Luo, Q.,… Ni, L. M.: FraudTrip: Taxi fraudulent trip detection from corresponding trajectories. IEEE Internet Things J. 8(16), 12505–12517 (2021)
Song, Y., Xin, R., Chen, P., Zhang, R., Chen, J., …, Zhao, Z.: Identifying performance anomalies in fluctuating cloud environments: a robust correlative-GNN-based explainable approach. Future Gener. Comput. Syst. 145, 77–86 (2023)
Ma, J., Hu, J.: Safe consensus control of cooperative-competitive multi-agent systems via differential privacy. Kybernetika 58(3), 426–439 (2022)
Siddiqui, S.A., Ahmad, A., Fatima, N.: IoT-based disease prediction using machine learning. Comput Electr Eng 108, 108675 (2023). https://doi.org/10.1016/j.compeleceng.2023.108675. (ISSN 0045-7906)
Luo, J., Zhao, C., Chen, Q., Li, G.: Using deep belief network to construct the agricultural information system based on Internet of Things. J. Supercomput 78(1), 379–405 (2022)
Shen, Y., Ding, N., Zheng, H.-T., Li, Y., Yang, M.: Modeling relation paths for knowledge graph completion. IEEE Trans Knowl Data Eng 33(11), 3607–3617 (2021)
Cheng, B., Zhu, D., Zhao, S., Chen, J.: Situation-aware IoT service coordination using the event-driven SOA paradigm. IEEE Trans. Netw. Serv. Manag. 13(2), 349–361 (2016)
Cheng, B., Wang, M., Zhao, S., Zhai, Z., Zhu, D., …, Chen, J.: Situation-Aware dynamic service coordination in an IoT environment. IEEE/ACM Trans. Netw. 25(4), 2082–2095 (2017)
Zhuang, Y., Jiang, N., Xu, Y., Xiangjie, K., Kong, X.: Progressive distributed and parallel similarity retrieval of large CT image sequences in mobile telemedicine networks. Wirel. Commun. Mob. Comput. (2022)
Zhang, Z., Wang, L., Zheng, W., Yin, L., Hu, R., … Yang, B.: Endoscope image mosaic based on pyramid ORB. Biomed. Signal Process. Control. 71, 103261 (2022)
Liu, Y., Tian, J., Hu, R., Yang, B., Liu, S., Yin, L., … Zheng, W.: Improved feature point pair purification algorithm based on SIFT during endoscope image stitching. Front. Neurorobot. (2022)
Lu, S., Yang, B., Xiao, Y., Liu, S., Liu, M., Yin, L.,… Zheng, W.: Iterative reconstruction of low-dose CT based on differential sparse. Biomed. Signal Process. Control 79, 104204 (2023)
Tang, Y., Liu, S., Deng, Y., Zhang, Y., Yin, L., … Zheng, W.: An improved method for soft tissue modeling. Biomed. Signal Process. Control 65 (2021)
Lu, S., Liu, S., Hou, P., Yang, B., Liu, M., Yin, L., … Zheng, W.: Soft tissue feature tracking based on deep matching network. Comput. Model. Eng. Sci. 136(1), 363–379 (2023)
Kumar, A., Singh, K., Khan, T., Ahmadian, A., Saad, M.H Md., Manjul, M.: ETAS: an efficient trust assessment scheme for BANs. IEEE Access 9, 83214–83233 (2021)
Khan, T., Singh, K., Shariq, M., Ahmad, K., Savita, K.S., Ahmadian, A., Salahshour, S., Conti, M.: An efficient trust-based decision-making approach for WSNs: machine learning oriented approach. Comput. Commun. 209, 217–229 (2023)
Cao, B., Wang, X., Zhang, W., Song, H., Lv, Z.: A many-objective optimization model of industrial internet of things based on private blockchain. IEEE Netw. 34(5), 78–83 (2020)
Ma, K., et al.: Reliability-constrained throughput optimization of industrial wireless sensor networks with energy harvesting relay. IEEE Internet Things J. 8(17), 13343–13354 (2021)
Ni, Q., Guo, J., Wu, W., Wang, H.: Influence-based community partition with sandwich method for social networks. IEEE Trans. Comput. Social Syst. 1–12 (2022)
Xie, X., Xie, B., Xiong, D., Hou, M., Zuo, J., Wei, G., … Chevallier, J.: New theoretical ISM-K2 Bayesian network model for evaluating vaccination effectiveness. J. Ambient Intell. Human. Comput. (2022)
Yan, L., Shi, Y., Wei, M., Wu, Y.: Multi-feature fusing local directional ternary pattern for facial expressions signal recognition based on video communication system. Alexandria Eng. J. 63, 307–320 (2023)
Maritta, A. V., Tella, L., Kirsi, H., Jaakko, V., Gaoming, L., Yao, T.,… Xianhong, L.: Measured and perceived impacts of evidence-based leadership in nursing: a mixed-methods systematic review protocol. BMJ Open. 11(10) (2021)
Xu, H., Han, S., Li, X., Han, Z.: Anomaly traffic detection based on communication-efficient federated learning in space-air-ground integration network. IEEE Trans. Wirel. Commun. (2023)
Zhao, Y., Hu, M., Jin, Y., Chen, F., Wang, X., Wang, B., …, Ren, H.: Predicting the transmission trend of respiratory viruses in new regions via geospatial similarity learning. Int. J. Appl. Earth Obs. Geoinf. 125, 103559 (2023)
Funding
No funding was obtained for this study.
Author information
Authors and Affiliations
Contributions
Jianjia Liu: Conceptualization, Methodology, Formal analysis, Supervision, Writing - original draft, Writing - review & editing.
Xin Yang: Supervision, Writing - original draft, Writing - review & editing.
Tiannan Liao: Writing - original draft, Writing - review & editing.
Yong Hang: Writing - original draft, Writing - review & editing.
Corresponding author
Ethics declarations
Ethics Approval and Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Competing Interests
The authors declare no competing interests.
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.
About this article
Cite this article
Liu, J., Yang, X., Liao, T. et al. An IoT-based Covid-19 Healthcare Monitoring and Prediction Using Deep Learning Methods. J Grid Computing 22, 26 (2024). https://doi.org/10.1007/s10723-024-09742-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10723-024-09742-w