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A computing system that integrates deep learning and the internet of things for effective disease diagnosis in smart health care systems

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Abstract

In the modern world, technology plays a major role in making processes easy, efficient, and mostly automated no matter where they take place. Technology such as artificial intelligence, the Internet of Things, blockchain, and deep learning has revolutionized the growth of many fields. One of the best examples is the medical field, in which timely and accurate diagnoses must be made routinely; traditional systems are of little help due to their lack of accuracy and the time delays they introduce. Instead, advancements in deep learning (DL) and the Internet of Things (IoT) are useful in building effective models for timely and accurate diagnosis and developing a smart health care system. In this paper, we propose a disease diagnosis model using DL in combination with IoT. The stages involved in the model are as follows: (a) Data are collected from various IoT wearable devices, in which sensors play a vital role in collecting data and relaying these data to DL systems for accurate diagnosis. (b) These medical data are preprocessed, as they contain noise. (c) The preprocessed data are passed to an isolation forest (iForest) for outlier detection with linear time complexity and high precision. (d) The data undergo a classification process, in which we use an integration of the particle swarm optimization algorithm and DenseNet169 (PSO-DenseNet169) to diagnose diseases; the parameters are tuned to improve accuracy. When we compared our proposed model to existing models such as SVM, KNN, NB-A, and J-48 based on performance parameters such as sensitivity, accuracy, and specificity, we found that our model outperformed the state of the art by 96.16% and 97.26% in diagnosing the heart and thyroid, respectively.

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Abbreviations

ML:

Machine learning

DL:

Deep learning

IoT:

Internet of Things

MRI:

Magnetic resonance imaging

CT:

Computed tomography

PET:

Positron emission tomography

WSN:

Wireless sensor network

WMS:

Wearable medical sensor

MES:

Micro-electromechanical systems

BSN:

Body sensor network

AI:

Artificial intelligence

EEG:

Electroencephalogram

ECG:

Electrocardiogram

WBSN:

Wireless body sensor network

LSTM-RNN:

Long short-term memory-recurrent neural network

HTM:

Hierarchical temporal memory

RF:

Random forest

KNN:

K nearest neighbor

SVM:

Support vector machine

NB:

Naïve Bayes

MAPEPK:

Monitor-analyze-plan-execute plus knowledge

HCAH:

Hierarchical computing architecture for healthcare

CNN:

Convolutional neural network

PSO:

Particle swarm optimization

References

  1. Deng L, Yu D (2014) Deep learning: methods and applications. Found Trends Signal Process 7(3–4):197–387

    Article  MathSciNet  Google Scholar 

  2. McCulloch WS,Pitts W (1943) A logical calculus of the ideas immanent in neurons activity. Bull Math Biophys 5:115–133

    Article  MathSciNet  Google Scholar 

  3. Jain AK, Mao J, Mohiuddin KM (1996) Artificial neural networks: a tutorial. IEEE Comput 29(3):31–44

    Article  Google Scholar 

  4. Chung K, Yoo H (2020) Edge computing health model using p2p-based deep neural networks. Peer-to-Peer Netw Appl 13:694–703

    Article  Google Scholar 

  5. Rumbold JMM, O’Kane M, Philip N, Pierscionek BK (2020) Big data and diabetes: the applications of big data for diabetes care now and in the future. Diabet Med 37(2):187–193

    Article  Google Scholar 

  6. Ray PP (2018) A survey on Internet of Things architectures. J King Saud Univ Comput Inf Sci 30:291–319

    Google Scholar 

  7. NFSO (2008) D.4 networked enterprise & RFID INFSO G.2 micro & nanosystems. In: Co-operation with the working group RFID of the ETP EPOSS, internet of Things in 2020, a roadmap for the future, version 1.1

  8. https://scikit-learn.org/

  9. Veeramakali T, Siva R, Sivakumar B, Mahesh PS, Krishnaraj N (2021) An intelligent internet of things-based secure healthcare framework using blockchain technology with an optimal deep learning model. J Supercomput:1–21

  10. Satpathy S, Mohan P, Das S, Debbarma S (2020) A new healthcare diagnosis system using an IoT-based fuzzy classifier with FPGA. J Supercomput 76(8):5849–5861

    Article  Google Scholar 

  11. Van Kranenburg R (2008) The Internet of Things: a critique of ambient technology and the all-seeing network of RFID. Institute of Network Cultures

  12. Bolhasani H, Jafarali JS (2020) Deep learning accelerators: a case study with MAESTRO. J Big Data 7:100

    Article  Google Scholar 

  13. Hu F, Xie D (2013) On the application of the Internet of Things in the field of medical and health care. In: IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and social computing

  14. Esteva A (2019) A guide to deep learning in healthcare. Nat Med 25:24–29

    Article  Google Scholar 

  15. Zhao R (2015) Deep learning and its applications to machine health monitoring: a survey. Mech Syst Signal Process 14(8):213–237

    Google Scholar 

  16. Nweke HF, The YW, Al-garage MA, Alo UR (2018) Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: state of the art and research challenges. Expert Syst Appl 105:233–261

    Article  Google Scholar 

  17. Pasluosta F (2018) Internet of Health Things: toward intelligent vital signs monitoring in hospital wards. Artif Intell Med 89:61–69

    Article  Google Scholar 

  18. Alam F (2017) Data fusion and IoT for smart ubiquitous environments: a survey. IEEE Access 5:9533–9554

    Article  Google Scholar 

  19. Riazul I (2015) The internet of Things for health care: a comprehensive survey. IEEE Access 3:678–708

    Article  Google Scholar 

  20. Abdellatif AA, Mohamed A, Chiasserini CF, Tlili M, Erbad A (2019) Edge computing for smart health: context-aware approaches, opportunities, and challenges. IEEE Netw 33(3):196–203

    Article  Google Scholar 

  21. Mutlag AA, Abd Ghani MK, Arunkumar N, Mohammed MA, Mohd O (2019) Enabling technologies for fog computing in healthcare IoT systems. Future Gener Comput Syst 90:62–78

    Article  Google Scholar 

  22. Abdulkareem KH, Mohammed MA, Salim A, Arif M, Geman O, Gupta D, Khanna A (2021) Realizing an effective COVID-19 diagnosis system based on machine learning and IoT in smart hospital environment. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2021.3050775

    Article  Google Scholar 

  23. Mutlag AA, Ghani MKA, Mohammed MA, Maashi MS, Mohd O, Mostafa SA, Abdulkareem KH, Marques G, Torre Díez de la I (2020) MAFC: Multi-agent fog computing model for healthcare critical tasks management. Sensors 20(7):1853

    Article  Google Scholar 

  24. Gupta S (2015) Diagnosis of diabetic retinopathy using machine learning. J Soft Comput. https://doi.org/10.4172/2311-3278

    Article  Google Scholar 

  25. Liang D, Yang F, Zhang T, Yang P (2018) Understanding mixup training methods. IEEE Access 6:58774–58783

    Article  Google Scholar 

  26. http://www.numpy.org/

  27. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61(1):85–117

    Article  Google Scholar 

  28. Ker J, Wang L, Rao J, Lim T (2018) Deep learning applications in medical image analysis. IEEE Access 6(1):9375–9389

    Article  Google Scholar 

  29. Hossain MS, Muhammad G (2018) Emotion-aware connected healthcare big data towards 5G. IEEE Internet Things J 5(4):2399–2406

    Article  Google Scholar 

  30. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp 770–778

  31. Yang H, Zhang J, Liu Q (2018) Multimodal MRI-based classification of migraine: using deep learning convolutional neural network. Biomed Eng Online 17(10):138

    Article  Google Scholar 

  32. Dogantekin E, Dogantekin A, Avci D (2011) An expert system based on generalized discriminant analysis and wavelet support vector machine for diagnosis of thyroid diseases. Expert Syst Appl 38(1):146–150

    Article  Google Scholar 

  33. Lakhani P, Sundaram B (2017) Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2):574–582

    Article  Google Scholar 

  34. Chen JZ, Ni D, Chou YH (2016) Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep 6(4):24454

    Article  Google Scholar 

  35. Khamparia A, Gupta D, de Albuquerque VHC, Sangaiah AK, Jhaveri RH (2020) Internet of health things-driven deep learning system for detection and classification of cervical cells using transfer learning. J Supercomput:1–19

  36. OpenCV Library opencv.org

  37. https://www.tensorflow.org/

  38. Villarrubia G, Bajo J, Paz De J, Corchado J (2014) Monitoring and detection platform to prevent anomalous situations in-home care. Sensors 14(6):9900–9921

    Article  Google Scholar 

  39. Sun W, Zheng B, Qian W (2017) Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput Biol Med 89:530–539

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Information Technology and Security, College of Computer Science and Information Technology, Jazan University, Jazan, Saudi Arabia

    Eshrag A. Refaee

  2. Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan, Saudi Arabia

    Shermin Shamsudheen

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  1. Eshrag A. Refaee
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  2. Shermin Shamsudheen
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Correspondence to Shermin Shamsudheen.

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Refaee, E.A., Shamsudheen, S. A computing system that integrates deep learning and the internet of things for effective disease diagnosis in smart health care systems. J Supercomput 78, 9285–9306 (2022). https://doi.org/10.1007/s11227-021-04263-9

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  • DOI: https://doi.org/10.1007/s11227-021-04263-9

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