Skip to main content
SpringerLink
Log in

IoT-Based Diseases Prediction and Diagnosis System for Healthcare

  • Chapter
  • First Online:
Internet of Things for Healthcare Technologies

Part of the book series: Studies in Big Data ((SBD,volume 73))

Abstract

Nowadays, developments in high-tech have led to the emergence of Internet of Things (IoT) and Artificial Intelligence (AI) applications in the healthcare industry. IoT devices such as smart pills, wearable monitors, and sensors allow to collect data continuously, and AI systems can use this data for diseases detection. In this chapter, through introducing machine learning and relation between machine learning and disease detection, especially on IoT data, the authors discuss machine learning techniques. Machine learning can analyze the extensive amount of information available on IoT devices, streamline the diagnostic process. The literature focuses on applied machine learning techniques on health devices’ data to diseases diagnosis and prediction. In this way, first of all, the authors mention the history of machine learning and some important and useful machine learning algorithms for healthcare usage; major objective of this chapter is describing machine learning methods and customized techniques on IoT data for disease detection. Then some real applied machine learning models in healthcare, are mentioned in this chapter. Future trends of machine learning using in disease detection are introduced through explaining a diagram about how IoT and AI work together to diseases diagnosis and prediction. Finally, the authors have summarized different sections of the chapter at the conclusion.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ain, Q. T., Ali, M., Riaz, A., Noureen, A., Kamran, M., Hayat, B., et al. (2017). Sentiment analysis using deep learning techniques: A review. International Journal of Advanced Computer Science and Applications, 8(6), 424.

    Google Scholar 

  2. Alsheref, F. K., & Gomaa, W. H. (2019). Blood diseases detection using classical machine learning algorithms. International Journal of Advanced Computer Science and Applications (IJACSA), 10(7).

    Google Scholar 

  3. Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J. (2013). Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine (De Gruyter Open), 11(2), 45–58.

    Google Scholar 

  4. Amit, B., Chinmay, C., Anand, K., & Debabrata, B. (2019). Emerging trends in IoT and big data analytics for biomedical and health care technologies. Handbook of data science approaches for biomedical engineering (Ch. 5, pp. 121–152). Elsevier. ISBN: 9780128183182.

    Google Scholar 

  5. Bengio, Y., LeCun, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539. PMID: 26017442.

    Article  Google Scholar 

  6. Beunza, J.-J., Puertas, E., García-Ovejero, E., Villalba, G., Condes, E., Koleva, G., et al. (2019). Comparison of machine learning algorithms for clinical event prediction. Elsevier. https://doi.org/10.1016/j.jbi.2019.10325.

  7. Carrio, A., Sampedro, C., Rodriguez-Ramos, A., & Campoy, P. (2017). A review of deep learning methods and applications for unmanned aerial vehicles. Hindawi Journal of Sensors. https://doi.org/10.1155/2017/329687463.

  8. Chai, Y., He, L., Mei, Q., Liu, H., & Xu, L. (2017). Deep learning through two-branch convolutional neuron network for glaucoma diagnosis. In Proceedings of International Conference on Smart Health (pp. 191–201). Springer.

    Google Scholar 

  9. Chakraborty, C., Gupta, B., & Ghosh, S. K. (2013). A review on telemedicine-based WBAN framework for patient monitoring. International Journal of Telemedicine and e-Health, 19(8), 619–626.

    Article  Google Scholar 

  10. Chandra kala, V., Venkateswarakiran, L., & Siva Prasad, P. (2019). Prediction of diseases with pathological characteristics classification using data mining. In International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), Vellore, India (pp. 1–5).

    Google Scholar 

  11. Chinmay, C. (2019). Computational approach for chronic wound tissue characterization. Informatics in Medicine Unlocked, 17, 1–10.

    Google Scholar 

  12. Daghistani, T. A., Elshawi, R., Sakr, S., Ahmed, A. M., Al-Thwayee, A., & Al-Mallah, M. H. (2019). Predictors of in-hospital length of stay among cardiac patients: A machine learning approach. International Journal of Cardiology. https://doi.org/10.1016/j.ijcard.2019.01.046.

    Article  Google Scholar 

  13. Deng, L., & Yu, D. (2014). Deep learning: Methods and applications. Foundations and Trends in Signal Processing, 7(3–4), 197–387. https://doi.org/10.1561/2000000039.

    Article  MathSciNet  MATH  Google Scholar 

  14. Devi, R. L., & Kalaivani, V. (2019). Machine learning and IoT-based cardiac arrhythmia diagnosis using statistical and dynamic features of ECG. Journal of Supercomputing. https://doi.org/10.1007/s11227-019-02873-y.

  15. Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM. https://doi.org/10.1145/2347736.2347755.

  16. Dong, Y., Wang, Q., Zhang, Q., & Yang, J. (2016). Classification of cataract fundus image based on retinal vascular information. In Proceedings of International Conference on Smart Health (pp. 166–173). Springer.

    Google Scholar 

  17. Fialho, A. S., Cismondi, F., Vieira, S. M., Reti, S. R., Sousa, J. M., & Finkelstein, S. N. (2012). Data mining using clinical physiology at discharge to predict ICU readmissions. Expert Systems with Applications, 39(18), 13158–13165.

    Article  Google Scholar 

  18. Fitriyani, N. L. (2019). Development of DPM based on ensemble learning approach for diabetes and hypertension. IEEE Access. Special section on data-enabled intelligence for digital health. https://doi.org/10.1109/ACCESS.2019.2945129.

  19. Ghasemi, F., Mehridehnavi, A. R., Fassihi, A., & Perez-Sanchez, H. (2017). Deep neural network in biological activity prediction using deep belief network. Applied Soft Computing, 62, 251.

    Google Scholar 

  20. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, MA: MIT Press.

    MATH  Google Scholar 

  21. Guo, Y. (2016). Deep learning for visual understanding. Neurocomputing, 187(26), 27–48.

    Article  Google Scholar 

  22. Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36–S40.

    Article  Google Scholar 

  23. Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527. PMID: 16764513.

    Article  MathSciNet  MATH  Google Scholar 

  24. Jánosi, A., Ofner, P., Branyickiné Géczy, G., & Polgár, P. (2013) Incidence of myocardial infarction in Hungary. Population study in five districts of Budapest and Szabolcs–Szatmar–Bereg County. Orvosi Hetilap, 154(28), 1106–1110.

    Google Scholar 

  25. Kober, J., Bagnell, J. A., & Peters, J. (2013). Reinforcement learning in robotics: A survey. The International Journal of Robotics Research, 32(11), 1238–1274. https://doi.org/10.1177/0278364913495721.

    Article  Google Scholar 

  26. Krizhevsky, A., & Hinton, G. E. (2011). Using very deep auto encoders for content based image retrieval. In 19th European Symposium on Artificial Neural Networks (ESANN’11), Bruges, Belgium.

    Google Scholar 

  27. Lillicrap, T. P., Hunt, J. J., & Pritzel, A. (2015). Continuous control with deep reinforcement learning. Cornel University Library. https://arxiv.org/abs/1509.02971.

  28. Liu, Y., & Choi, K. S. (2017). Using machine learning to diagnose bacterial sepsis in the critically ill patients. In Proceedings of International Conference on Smart Health (pp. 223–233). Springer.

    Google Scholar 

  29. Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018, August). Machine learning for internet of things data analysis: a survey. Digital Communications and Networks, 4(3), 161–175. https://doi.org/10.1016/j.dcan.2017.10.002.

  30. Makino, M., et al. (2019). Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. Scientific Reports. https://doi.org/10.1038/s41598-019-48263-5.

    Article  Google Scholar 

  31. Mitchell, T. (1997). Machine learning (p. 2). New York, NY: McGraw Hill. ISBN: 978-0-07-042807-2.

    Google Scholar 

  32. Piros, P., Ferenci, T., Fleiner, R., Andréka, P., Fujita, H., Főző, L., et al. (2019). Comparing machine learning and regression models for mortality prediction based on the Hungarian Myocardial Infarction Registry. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2019.04.027.

  33. Prusa, J. D., & Khoshgoftaar, T. M. (2017). Improving deep neural network design with new text data representations. Big Data, 4(1), 7. https://doi.org/10.1186/s40537-017-0065-8.

    Article  Google Scholar 

  34. Rayan, Z., Alfonse, M., & Salem, A.-B. M. (2019). Machine learning approaches in smart health. In 8th International Congress of Information and Communication Technology, ICICT 2019. Elsevier.

    Google Scholar 

  35. Urban, G. (2018). Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology, 155, 1069–1078.e8.

    Article  Google Scholar 

  36. Vanani, I. R., & Amirhosseini, M. (2019). Deep learning for opinion mining. Extracting knowledge from opinion mining (pp. 40–65). Hershey, PA: IGI Global.

    Google Scholar 

  37. Viegas, R., Salgado, C. M., Curto, S., Carvalho, J. P., Vieira, S. M., & Finkelstein, S. N. (2017). Daily prediction of ICU readmissions using feature engineering and ensemble fuzzy modeling. Expert Systems with Applications, 79, 244–253.

    Article  Google Scholar 

  38. Vincent, P., Larochelle, H., Lajoie, I., & Manzagol, P. (2010). Stacked de noising auto encoders: Learning useful representations in deep network with a local de noising criterion. Journal of Machine Learning Research, 11, 3371–3408.

    MATH  Google Scholar 

  39. Zhang, J., Luo, Y., Jiang, Z., & Tang, X. (2017). Regression analysis and prediction of mini-mental state examination score in Alzheimer’s disease using multi-granularity whole-brain segmentations. In Proceedings of International Conference on Smart Health (pp. 202–213). Springer.

    Google Scholar 

  40. Zheng, B., Zhang, J., Yoon, S. W., Lam, S. S., Khasawneh, M., & Poranki, S. (2015). Predictive modeling of hospital readmissions using met heuristics and data mining. Expert Systems with Applications, 42(20), 7110–7120.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Allameh Tabataba’i University (ATU), Tehran, Iran

    Iman Raeesi Vanani & Morteza Amirhosseini

Authors
  1. Iman Raeesi Vanani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Morteza Amirhosseini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Iman Raeesi Vanani .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, India

    Chinmay Chakraborty

  2. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Amit Banerjee

  3. Department of Electrical Engineering, Indian Institute of Technology Patna, Patna, India

    Maheshkumar H. Kolekar

  4. Department of Computer Information Systems, Faculty of Information and Communication Technology, University of Malta, Msida, Malta

    Lalit Garg

  5. Faculty of Software and Information Science, Iwate Prefectural University, Takizawa, Japan

    Basabi Chakraborty

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Raeesi Vanani, I., Amirhosseini, M. (2021). IoT-Based Diseases Prediction and Diagnosis System for Healthcare. In: Chakraborty, C., Banerjee, A., Kolekar, M., Garg, L., Chakraborty, B. (eds) Internet of Things for Healthcare Technologies. Studies in Big Data, vol 73. Springer, Singapore. https://doi.org/10.1007/978-981-15-4112-4_2

Download citation

Publish with us

Policies and ethics

Search

Navigation