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A smart Alzheimer’s patient monitoring system with IoT-assisted technology through enhanced deep learning approach

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Abstract

Earlier detection of Alzheimer’s disease is more significant for improving the quality of the patient’s life. This aspect may reduce the fatality rate among the population and also maximize the average life expectancy. Thus, this paper introduces a new Alzheimer's prediction model using IoT and deep structured architectures. A new smart Alzheimer’s patient monitoring system is developed by processing healthcare data using IoT devices. Initially, Alzheimer’s patients are detected from the set of patients using “enhanced deep residual network–long short-term memory (DRN-LSTM).” Here, the detection process is done with the data associated with the patients. The optimal feature selection phase and enhanced deep convolutional network (DCN) and deep residual network (DRN)-based detection are accomplished by parameter-improved horse herd optimization algorithm (PI-HHO). The monitored data involve audio, data, and video from the sensors based on the location and movements of patients. Next, the gathered data are forwarded to the optimal feature selection with the same algorithm and predicted the abnormalities through enhanced DNN + LSTM using PI-HHO. Thirdly, the abnormal patients are alerted to the nearby hospital for appropriate treatment and monitoring. All through the result evaluation, the accuracy and precision rate of the recommended Alzheimer’s patient monitoring system attain 98% and 97%. Thus, this smart patient prediction model ensures the high-quality results in terms of standard performance metrics while evaluating with other algorithms.

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Data availability

The data underlying this article are available in https://catalog.data.gov/dataset/alzheimers-disease-and-healthy-aging-ata, https://archive.ics.uci.edu/ml/datasets/Daphnet+Freezing+of+Gaitandhttps://www.kaggle.com/hyunseokc/detecting-early-alzheimer-s/data.

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Acknowledgements

This work was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2023R79) and Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Funding

This work was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2023R79) and Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamilnadu, 602105, India

    Rajesh Arunachalam

  2. Department of AI & ML, School of Computing, Mohan Babu University, Tirupati, Andhra Pradesh, India

    Gurram Sunitha

  3. Department of Computer Engineering, SVKM’S NMIMS MPSTME Shirpur Campus, Shirpur, India

    Surendra Kumar Shukla

  4. Department of Pharmacology, College of Pharmacy, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, 244001, India

    Surya Nath pandey

  5. Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

    Shabana Urooj

  6. Department of Information Technology & Engineering, Amity University, Tashkent, Uzbekistan

    Seema Rawat

Authors
  1. Rajesh Arunachalam
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  2. Gurram Sunitha
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  3. Surendra Kumar Shukla
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  4. Surya Nath pandey
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  5. Shabana Urooj
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  6. Seema Rawat
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Contributions

SU and RA contributed to conceptualization, methodology, software, data curation, writing—original draft preparation. GS and SP contributed to visualization and investigation. SKS contributed to software and validation. SU, SR, and RA contributed to writing—reviewing and editing.

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Correspondence to Shabana Urooj.

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Arunachalam, R., Sunitha, G., Shukla, S.K. et al. A smart Alzheimer’s patient monitoring system with IoT-assisted technology through enhanced deep learning approach. Knowl Inf Syst 65, 5561–5599 (2023). https://doi.org/10.1007/s10115-023-01890-x

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  • DOI: https://doi.org/10.1007/s10115-023-01890-x

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