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ISSN (Online): 2212-4047

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Research Article

Collective Diagnostic Prototypical in Internet of Medical Things for Depression Identification using Deep Learning Algorithm

Author(s): Shitharth Selvarajan*, Manikandan Thirumalaisamy, Hariprasath Manoharan, Ramkumar Devendiran, S. Lakshmi Narayanan and Ashwani Kumar

Volume 18, Issue 9, 2024

Published on: 05 October, 2023

Article ID: e051023221792 Pages: 10

DOI: 10.2174/0118722121240855230926034148

Price: $65

Abstract

Background: The majority of wearable technology that is present in various patents for Internet of Medical Things (IoMT) health monitoring systems is introduced to recognize various bodily indicators. The enumerated patents indicate that monitored values are sent to a central server, where they are all treated by experts at the appropriate moment. Therefore, a new patent technique by expanding the use of wireless devices, has been discovered that such communication technologies can recognize specific depression traits and mood swings.

Objectives: The major objective of the proposed method is to analyze the disputes that arise in the characteristics of an individual by observing the leveling periods that are identified from the processed image. In addition, the rate of data transfer in case of any dispute is maximized therefore recognition problem is solved at a minimized distance. Further, the steady state probability values are achieved at low delay thus minimizing the dropout packets in the monitored system using IoMT and LSTM.

Methods: A balanced record with four distinct parameters—such as livelihood, self-reliance, correlation, and precision—is employed with the projected model on IoMT for depression identification. As a result, high data transfer rates and low distance separation are used to process the identification framework. Additionally, by combining an original matrix representation with the input feature set using LSTM, a novel framework with great efficiency is created.

Results: In order to assess the results of IoMT using LSTM, four situations are split apart and their probability ratios are calculated. The results of each situation are then contrasted with the current methodology, and it is found that when there is a low dropout ratio, depression in a person is quickly diagnosed.

Conclusion: The comparison analysis demonstrates that the proposed method, when compared to the current method, offers the best-compromised outcomes at roughly 64%.

Graphical Abstract

[1]
A. Pampouchidou, P.G. Simos, K. Marias, F. Meriaudeau, F. Yang, M. Pediaditis, and M. Tsiknakis, "Automatic assessment of depression based on visual cues: A systematic review", IEEE Trans. Affect. Comput., vol. 10, no. 4, pp. 445-470, 2019.
[http://dx.doi.org/10.1109/TAFFC.2017.2724035]
[2]
M.T. Masud, M.A. Mamun, K. Thapa, D.H. Lee, M.D. Griffiths, and S.H. Yang, "Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone", J. Biomed. Inform., vol. 103, no. January, p. 103371, 2020.
[http://dx.doi.org/10.1016/j.jbi.2019.103371] [PMID: 31935462]
[3]
A.H. Yazdavar, "Semi-Supervised approach to monitoring clinical depressive symptoms in social media", Proc. 2017 IEEE/ACM Int. Conf. Adv. Soc. Networks Anal. Mining, ASONAM 2017, pp. 1191-1198, 2017.
[http://dx.doi.org/10.1145/3110025.3123028]
[4]
R. Safa, P. Bayat, and L. Moghtader, "Automatic detection of depression symptoms in twitter using multimodal analysis", J. Supercomput., vol. 78, no. 4, pp. 4709-4744, 2022.
[http://dx.doi.org/10.1007/s11227-021-04040-8]
[5]
J. Srivastava, S. Routray, S. Ahmad, and M.M. Waris, "Internet of Medical Things (IoMT)-based smart healthcare system: Trends and progress", Comput. Intell. Neurosci., vol. 2022, pp. 1-17, 2022.
[http://dx.doi.org/10.1155/2022/7218113] [PMID: 35880061]
[6]
S. El Kafhali, and K. Salah, "Performance modelling and analysis of Internet of Things enabled healthcare monitoring systems", IET Netw., vol. 8, no. 1, pp. 48-58, 2019.
[http://dx.doi.org/10.1049/iet-net.2018.5067]
[7]
S. Shukla, M.F. Hassan, M.K. Khan, L.T. Jung, and A. Awang, "An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment", PLoS One, vol. 14, no. 11, p. e0224934, 2019.
[http://dx.doi.org/10.1371/journal.pone.0224934]
[8]
M.B. Morshed, K. Saha, R. Li, S.K. D’Mello, M. De Choudhury, G.D. Abowd, and T. Plötz, "Prediction of mood instability with passive sensing", Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 3, no. 3, pp. 1-21, 2019.
[http://dx.doi.org/10.1145/3351233]
[9]
N. Shi, D. Zhang, L. Li, and S. Xu, "Predicting mental health problems with automatic identification of metaphors", J. Healthc. Eng., vol. 2021, pp. 1-11, 2021.
[http://dx.doi.org/10.1155/2021/5582714] [PMID: 34012545]
[10]
L. Wang, Y. Ali, S. Nazir, and M. Niazi, "ISA evaluation framework for security of internet of health things system using ahp-topsis methods", IEEE Access, vol. 8, pp. 152316-152332, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.3017221]
[11]
U. Khan, A. Ali, S. Khan, F. Aadil, M.Y. Durrani, K. Muhammad, R. Baik, and J.W. Lee, "Internet of Medical Things–based decision system for automated classification of Alzheimer’s using three-dimensional views of magnetic resonance imaging scans", Int. J. Distrib. Sens. Netw., vol. 15, no. 3, 2019.
[http://dx.doi.org/10.1177/1550147719831186]
[12]
O.M. Mirza, H. Mujlid, H. Manoharan, S. Selvarajan, G. Srivastava, and M.A. Khan, "Mathematical framework for wearable devices in the internet of things using deep learning", Diagnostics (Basel), vol. 12, no. 11, p. 2750, 2022.
[http://dx.doi.org/10.3390/diagnostics12112750] [PMID: 36359592]
[13]
S.A. Wagan, J. Koo, I.F. Siddiqui, M. Attique, D.R. Shin, and N.M.F. Qureshi, "“Internet of medical things and trending converged technologies: A comprehensive review on real-time applications”, J. King Saud Univ. -", Comput. Inf. Sci, vol. 34, no. 10, pp. 9228-9251, 2022.
[http://dx.doi.org/10.1016/j.jksuci.2022.09.005]
[14]
S. Forrest, K. Baker, and M. Ketel, "Internet of medical things: Enabling key technologies", Conf. Proc. - IEEE SOUTHEASTCON, vol. 2021, 2021.
[http://dx.doi.org/10.1109/SoutheastCon45413.2021.9401862]
[15]
P. Kumar Das, F. Zhu, S. Chen, C. Luo, P. Ranjan, and G. Xiong, "Smart medical healthcare of internet of medical things (IOMT): Application of non-contact sensing", Proc. 14th IEEE Conf. Ind. Electron. Appl. ICIEA 2019, pp. 375-380, 2019.
[http://dx.doi.org/10.1109/ICIEA.2019.8833992]
[16]
A. Mali, A.G. Ororbia, D. Kifer, and C.L. Giles, "Recognizing and verifying mathematical equations using multiplicative differential neural units", 35th AAAI Conf. Artif. Intell. AAAI 2021, pp. 5006-5015, 2021.
[http://dx.doi.org/10.1609/aaai.v35i6.16634]
[17]
S. Malakar, S. Goswami, B. Ganguli, A. Chakrabarti, S.S. Roy, K. Boopathi, and A.G. Rangaraj, "Designing a long short-term network for short-term forecasting of global horizontal irradiance", SN Applied Sciences, vol. 3, no. 4, p. 477, 2021.
[http://dx.doi.org/10.1007/s42452-021-04421-x]
[18]
S. Shitharth, A.O. Khadidos, A.M. Alshareef, H. Manoharan, and A.O. Khadidos, "Application of improved support vector machine for pulmonary syndrome exposure with computer vision measures", Curr. Bioinform., vol. 18, pp. 1-13, 2023.
[http://dx.doi.org/10.2174/1574893618666230206121127]
[19]
T. Hasanin, P.R. Kshirsagar, H. Manoharan, S.S. Sengar, S. Selvarajan, and S.C. Satapathy, "Exploration of despair eccentricities based on scale metrics with feature sampling using a deep learning algorithm", Diagnostics (Basel), vol. 12, no. 11, p. 2844, 2022.
[http://dx.doi.org/10.3390/diagnostics12112844]
[20]
M. Chopra, “United States Patent : 5861366 United States Patent : 5861366,” New York, vol. 1, no. 19, pp. 1–29, 2010, [Online]. Available: https://patentimages.storage.googleapis.com/30/f4/62/e9b75605352fb0/US10679987.pdf
[21]
Ruud J., "United States Patent : 3871965 United States Patent : 3871965", Yeast, vol. 2, no. 19, pp. 4-6, 2020.

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