Abstract
Diarrhea is one of the most common infectious diseases that affect people of all ages and is a serious public health concern around the world. The main causes of diarrhea include food quality, water, indoor meteorological, and outdoor meteorological conditions. In this study, a dew computing-assisted smart monitoring framework is developed to evaluate the relationship among the health, indoor meteorological, and food factors of an individual to predict the cause of diarrhea with the scale of severity. Smart sensors are utilized at the physical layer to collect the targeted parameters of health, indoor meteorological, and food of the individual. The captured events are classified at the cyber layer by utilizing the Probabilistic Weighted-Naïve Bayes (PW-NB) classification approach for quantifying abnormal health events. Furthermore, a Multi-scale Gated Recurrent Unit (M-GRU) is suggested to obtain the scale of severity by analyzing the correlation between irregular health, food, and environmental events. In this manner, the proposed model M-GRU has achieved a high precision value of (\(93.26\%\)), whereas, LSTM, RNN, SVM achieved the precision value of (\(89.13\%\)), (\(90.43\%\)), (\(88.23\%\)), respectively. In addition, the precision value of the PW-NB is (\(97.15\%\)), which is also higher as compared to KNN (\(93.25\%\)) and DT (\(96.91\%\)). The outcome of the proposed solutions is shown the higher Precision values on dew computing and cloud computing. Moreover, a comparative analysis defines the prediction effectiveness of the proposed solution over several other decision-making solutions with regards to event classification, severity determination, monitoring stability, and prediction efficiency.
Similar content being viewed by others
References
Wu T, Perrings C, Kinzig A, Collins JP, Minteer BA, Daszak P (2017) Economic growth, urbanization, globalization, and the risks of emerging infectious diseases in China: a review. Ambio 46(1):18–29
“Diarrhoeal disease.” https://www.who.int/news-room/fact-sheets/detail/diarrhoeal-disease (accessed Mar. 06, 2021)
Liu L, Johnson HL, Cousens S, Perin J, Scott S, Lawn, JE,... & Child Health Epidemiology Reference Group of WHO and UNICEF. (2012) Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. The Lancet 379(9832), 2151-2161
Shine S, Muhamud S, Adanew S, Demelash A, Abate M (2020) Prevalence and associated factors of diarrhea among under-five children in Debre Berhan town, Ethiopia 2018: a cross sectional study. BMC Infect Dis 20(1):1–6
Lu Y (2017) Industry 4.0: A survey on technologies, applications and open research issues. J Ind Inf Integr 6:1–10
Chang C, Srirama SN, Buyya R (2017) Indie fog: An efficient fog-computing infrastructure for the internet of things. Computer 50(9):92–98
Subramaniyaswamy V, Manogaran G, Logesh R, Vijayakumar V, Chilamkurti N, Malathi D, Senthilselvan N (2019) An ontology-driven personalized food recommendation in IoT-based healthcare system. J Supercomput 75(6):3184–3216
Zhang Q, Bai C, Chen Z, Li P, Yu H, Wang S, Gao H (2021) Deep learning models for diagnosing spleen and stomach diseases in smart Chinese medicine with cloud computing. Concurr Comput Pract Exp 33(7):1–1
Yue, L., Tian, D., Chen, W., Han, X., & Yin, M. (2020). Deep learning for heterogeneous medical data analysis. World Wide Web, 1-23
Hu Y, Xu Z, Jiang F, Li S, Liu S, Wu M, Tong S (2020) Relative impact of meteorological factors and air pollutants on childhood allergic diseases in Shanghai. China. Science of The Total Environment 706:135975
Wang Y, Li J, Gu J, Zhou Z, Wang Z (2015) Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (China). Appl Soft Comput 35:280–290
Wang, Z., Huang, Y., He, B., Luo, T., Wang, Y., & Fu, Y. (2020). Short-term infectious diarrhea prediction using weather and search data in Xiamen, China. Scientific Programming, 2020
He C, Fan X, Li Y (2012) Toward ubiquitous healthcare services with a novel efficient cloud platform. IEEE Trans Biomed Eng 60(1):230–234
Thakar, A. T., & Pandya, S. (2017, July). Survey of IoT enables healthcare devices. In 2017 International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1087-1090). IEEE
Ray PP (2017) An introduction to dew computing: definition, concept and implications. IEEE Access 6:723–737
Rindos, A., & Wang, Y. (2016, October). Dew computing: The complementary piece of cloud computing. In 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom)(BDCloud-SocialCom-SustainCom) (pp. 15-20). IEEE
Singh, A., & Kumar, R. (2020, February). Heart disease prediction using machine learning algorithms. In 2020 international conference on electrical and electronics engineering (ICE3) (pp. 452-457). IEEE
Shetaban S, Seyyed Esfahani MM, Saghaei A, Ahmadi A (2020) Operations research and health systems: A literature review. Journal of Industrial Engineering and Management Studies 7(2):240–260
Azimi, I., Takalo-Mattila, J., Anzanpour, A., Rahmani, A. M., Soininen, J. P., & Liljeberg, P. (2018, September). Empowering healthcare iot systems with hierarchical edge-based deep learning. In Proceedings of the 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (pp. 63-68)
Mahmud, R., Koch, F. L., & Buyya, R. (2018, January). Cloud-fog interoperability in IoT-enabled healthcare solutions. In Proceedings of the 19th international conference on distributed computing and networking (pp. 1-10)
Mois G, Folea S, Sanislav T (2017) Analysis of three IoT-based wireless sensors for environmental monitoring. IEEE Trans Instrum Meas 66(8):2056–2064
Senthilkumar R, Venkatakrishnan P, Balaji N (2020) Intelligent based novel embedded system based IoT enabled air pollution monitoring system. Microprocess Microsyst 77:103172
Benammar M, Abdaoui A, Ahmad SH, Touati F, Kadri A (2018) A modular IoT platform for real-time indoor air quality monitoring. Sensors 18(2):581
Salamone F, Danza L, Meroni I, Pollastro MC (2017) A low-cost environmental monitoring system: How to prevent systematic errors in the design phase through the combined use of additive manufacturing and thermographic techniques. Sensors 17(4):828
Frank, E., Hall, M., & Pfahringer, B. (2012). Locally weighted naive bayes. arXiv preprint arXiv:1212.2487
Fayyad, U., & Irani, K. (1993). Multi-interval discretization of continuous-valued attributes for classification learning
Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113
Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press, Cambridge
Cho K, Van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2006) Learning Phrase Representations using RNN Encoder - Decoder for Statistical Machine Translation. J Biol Chem. https://doi.org/10.1074/jbc.M608066200
Abdullahi, T., & Nitschke, G. (2021, June). Predicting Disease Outbreaks with Climate Data. In 2021 IEEE Congress on Evolutionary Computation (CEC) (pp. 989-996). IEEE
Pizzulli, V. A., Telesca, V., & Covatariu, G. (2021, January). Analysis of Correlation between Climate Change and Human Health Based on a Machine Learning Approach. In Healthcare (Vol. 9, No. 1, p. 86). Multidisciplinary Digital Publishing Institute
Yu Z, Amin SU, Alhussein M, Lv Z (2021) Research on disease prediction based on improved DeepFM and IoMT. IEEE Access 9:39043–39054
Jagadeeswari V, Subramaniyaswamy V, Logesh R, Vijayakumar V (2018) A study on medical Internet of Things and Big Data in the personalized healthcare system. Health information science and systems 6(1):1–20
Hasan, M. M., Faruk, M. O., Biki, B. B., Riajuliislam, M., Alam, K., & Shetu, S. F. (2021, January). Prediction of Pneumonia Disease of Newborn Baby Based on Statistical Analysis of Maternal Condition Using Machine Learning Approach. In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 919-924). IEEE
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Authors declare that they have no conflict of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Afaq, Y., Manocha, A. Dew computing-assisted cognitive Intelligence-inspired smart environment for diarrhea prediction. Computing 104, 2511–2540 (2022). https://doi.org/10.1007/s00607-022-01097-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00607-022-01097-y