Skip to content
2000
image of Integrating IoMT and Federated Learning for Advanced Healthcare Monitoring in Healthcare 5.0
file format pdf download PDF

Abstract

Introduction

The Internet of Medical Things (IoMT) has made it possible to create advanced health monitoring systems. It allows the system to detect problems early, thereby mitigating long-term effects. This development will likely enhance the quality of healthcare professionals by reducing their workload and healthcare costs. The IoT in medical technology offers a wide range of information technology capabilities, including intelligent and collaborative healthcare solutions. Aggregating health data in a single repository raises security, copyright, and compliance issues when building a complex machine-learning model.

Method

Federated learning overcomes the above challenges by dispersing a global learning model through a central aggregate server. It retains mastery of patient data in a local participant who ensures data privacy and integrity. This research aims to develop an advanced healthcare monitoring system utilizing federated learning techniques. The system is designed to enable healthcare providers to effectively track patient health through medical sensors and respond promptly when necessary.

Results

The federated learning-based XGBoost model achieved a predictive accuracy of 97.2% in diagnosing Parkinson’s disease. Additionally, the system demonstrated improved privacy preservation, significantly reducing sensitive data exposure with minimal computational overhead, confirming its practical effectiveness in clinical scenarios.

Discussion

By leveraging federated learning, the proposed approach seeks to enhance the efficiency and effectiveness of health monitoring in clinical settings. To achieve accurate classification and early detection of Parkinson's disease, the study employs two key machine learning algorithms: Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost). These methods were selected for their statistical robustness and suitability for the task at hand.

Conclusion

The combination of federated learning, SVM, and XGBoost enhances healthcare monitoring and ensures patient data privacy and integrity.

© 2025 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
Loading

Article metrics loading...

/content/journals/cpb/10.2174/0113892010380715250728143022
2025-08-08
2025-11-03

  • From This Site

    /content/journals/cpb/10.2174/0113892010380715250728143022
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/cpb/10.2174/0113892010380715250728143022/BMS-CPB-2024-HT45-6526-1.html?itemId=/content/journals/cpb/10.2174/0113892010380715250728143022&mimeType=html&fmt=ahah

References

  1. Yu Z. Amin S.U. Alhussein M. Lv Z. Research on disease prediction based on improved DeepFM and IoMT. IEEE Access 2021 9 39043 39054 10.1109/ACCESS.2021.3062687
    [Google Scholar]
  2. Hammad M. Iliyasu A.M. Elgendy I.A. El-Latif A.A.A. End-to-end data authentication deep learning model for securing IoT configurations. Human-centric Comput. Inform. Sci. 2022 12 10.22967/HCIS.2022.12.004
    [Google Scholar]
  3. Babar M. Khan M.S. Habib U. Shah B. Ali F. Song D. Scalable edge computing for iot and multimedia applications using machine learning. Human-centric Comput. Inform. Sci. 2021 11 10.22967/HCIS.2021.11.041
    [Google Scholar]
  4. Park J. Salim M.M. Jo J.H. Sicato J.C.S. Rathore S. Park J.H. CIoT-Net: A scalable cognitive IoT based smart city network architecture. Human-centric Comput. Inform. Sci. 2019 9 1 29 10.1186/s13673‑019‑0190‑9
    [Google Scholar]
  5. Uddin Z. Ahmad A. Qamar A. Altaf M. Recent advances of the signal processing techniques in future smart grids. Hum. Cent Comput. Inf Sci. 2018 8 2 10.1186/s13673‑018‑0126‑9
    [Google Scholar]
  6. Yu B. Sun F. Chen C. Fu G. Hu L. Power demand response in the context of smart home application. Energy. 2022 240 122774 10.1016/j.energy.2021.122774
    [Google Scholar]
  7. Massaoudi M. Abu-Rub H. Refaat S.S. Chihi I. Oueslati F.S. Deep learning in smart grid technology: A review of recent advancements and future prospects. IEEE Access 2021 9 54558 54578 10.1109/ACCESS.2021.3071269
    [Google Scholar]
  8. Nandy S. Adhikari M. Khan M.A. Menon V.G. Verma S. An intrusion detection mechanism for secured IoMT framework based on swarm-neural network. IEEE J. Biomed. Health. Inform. 2022 26 5 1969 1976 10.1109/JBHI.2021.3101686 34357873
    [Google Scholar]
  9. Zaman S. Khandaker M.R.A. Khan R.T. Tariq F. Wong K.K. Thinking out of the blocks: Holochain for distributed security in IoT healthcare. IEEE Access 2022 10 37064 37081 10.1109/ACCESS.2022.3163580
    [Google Scholar]
  10. Zarour M. Alenezi M. Ansari M.T.J. Pandey A.K. Ahmad M. Agrawal A. Kumar R. Khan R.A. Ensuring data integrity of healthcare information in the era of digital health. Healthc Technol Lett 2021 8 3 66 77 10.1049/htl2.12008 34035927
    [Google Scholar]
  11. Pallathadka H. Mustafa M. Sanchez D.T. Sekhar Sajja G. Gour S. Naved M. IMPACT OF MACHINE learning ON Management, healthcare AND AGRICULTURE. Mater. Today Proc 2023 80 2803 2806 10.1016/j.matpr.2021.07.042
    [Google Scholar]
  12. Hulsen T. Explainable Artificial Intelligence (XAI): Concepts and challenges in healthcare. AI 2023 652 652 666 10.3390/ai4030034
    [Google Scholar]
  13. Dhiman P. Bonkra A. Sehgal A.K. Dhanaraj R.K. Healthcare trust evolution with explainable artificial intelligence: Bibliometric analysis. Information 2023 14 10 541 10.3390/info14100541
    [Google Scholar]
  14. Javed A.R. Sarwar M.U. Beg M.O. Asim M. Baker T. Tawfik H. A collaborative healthcare framework for shared healthcare plan with ambient intelligence. Human-centric Comput. Inform. Sci. 2020 10 1 40 10.1186/s13673‑020‑00245‑7
    [Google Scholar]
  15. Tanwar M. Khatri S.K. Pendse R. A framework for feature selection using natural language processing for user profile learning for recommendations of healthcare-related content. Int. J. Bus Anal. 2022 9 3 1 17 10.4018/IJBAN.292059
    [Google Scholar]
  16. Wiens J. Shenoy E.S. Machine learning for healthcare: On the verge of a major shift in healthcare epidemiology. Clin. Infect. Dis. 2018 66 1 149 153 10.1093/cid/cix731 29020316
    [Google Scholar]
  17. Jiang F. Jiang Y. Zhi H. Dong Y. Li H. Ma S. Wang Y. Dong Q. Shen H. Wang Y. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol. 2017 2 4 230 243 10.1136/svn‑2017‑000101 29507784
    [Google Scholar]
  18. Mazhar T. khan S. Shahzad T. khan M.A. Saeed M.M. Awotunde J.B. Hamam H. Generative AI, IoT, and blockchain in healthcare: Application, issues, and solutions. Discover Internet of Things 2025 5 1 5 10.1007/s43926‑025‑00095‑8
    [Google Scholar]
  19. Srinivasu P. N. Sandhya N. Jhaveri R. H. Raut R. From blackbox to explainable AI in healthcare: Existing tools and case studies. Mob Inf Syst. 2022 2022 1 1 20 Jun 10.1155/2022/8167821
    [Google Scholar]
  20. Yaqoob M.M. Alsulami M. Khan M.A. Alsadie D. Saudagar A.K.J. AlKhathami M. Khattak U.F. Symmetry in privacy-based healthcare: A review of skin cancer detection and classification using federated learning. Symmetry 2023 15 7 1369 10.3390/sym15071369
    [Google Scholar]
  21. Ahmed D. Neema R. Viswanadha N. Selvanambi R. Analysis and prediction of healthcare sector stock price using machine learning techniques. Int. J. Inf Syst. Model Des 2022 13 9 1 15 10.4018/IJISMD.303131
    [Google Scholar]
  22. Davenport T. Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019 6 2 94 98 10.7861/futurehosp.6‑2‑94 31363513
    [Google Scholar]
  23. Hasan T. Bin Karim M.F. Mahadi M.K. Nishat M.M. Faisal F. Employment of ensemble machine learning methods for human activity recognition. J. Healthc Eng. 2022 2022 1 18 10.1155/2022/6963891 36199373
    [Google Scholar]
  24. Dhahri H. Al Maghayreh E. Mahmood A. Elkilani W. Faisal Nagi M. Automated breast cancer diagnosis based on machine learning algorithms. J. Healthc Eng. 2019 2019 4253641 10.1155/2019/4253641 31814951
    [Google Scholar]
  25. Tasin I. Nabil T.U. Islam S. Khan R. Diabetes prediction using machine learning and explainable AI techniques. Healthc Technol Lett 2023 10 1-2 1 10 10.1049/htl2.12039 37077883
    [Google Scholar]
  26. Khan F. Tariq M. Khan Z.A. Ur Rehman A. Abbas S. Cloud-based breast cancer prediction empowered with soft computing approaches. Article 8017496 J. Healthc Eng. 2020 2020 10.1155/2020/8017496
    [Google Scholar]
  27. Abbas S. Khan M.A. Falcon-Morales L.E. Rehman A. Saeed Y. Zareei M. Zeb A. Mohamed E.M. Modeling, simulation and optimization of power plant energy sustainability for IoT enabled smart cities empowered with deep extreme learning machine. IEEE Access 2020 8 39982 39997 10.1109/ACCESS.2020.2976452
    [Google Scholar]
  28. Son Y.J. Kim H.G. Kim E.H. Choi S. Lee S.K. Application of support vector machine for prediction of medication adherence in heart failure patients. Healthc Inform. Res. 2010 16 4 253 259 10.4258/hir.2010.16.4.253 21818444
    [Google Scholar]
  29. Iyortsuun N. K. Kim S. H. Jhon M. Yang H. J. Pant S. A review of machine learning and deep learning approaches on mental health diagnosis. Healthcare 2023 11 3 285 10.3390/healthcare11030285
    [Google Scholar]
  30. Salas-Zárate R. Alor-Hernández G. Salas-Zárate M. D. P. Paredes-Valverde M. A. Bustos-López M. Sánchez-Cervantes J. L. Detecting depression signs on social media: A systematic literature review. Healthc 2022 10 2 291 Feb 10.3390/healthcare10020291 35206905
    [Google Scholar]
  31. Khan M.A. Challenges facing the application of iot in medicine and healthcare. Int. J. Comput. Inform. Manufact 2021 1 1 10.54489/ijcim.v1i1.32
    [Google Scholar]
  32. Albassam A. Ijaz M. Asghar M. Integration of blockchain and cloud computing in telemedicine and healthcare. Int. J. Comput. Sci. Netw Secur. 2023 23 6 59 66
    [Google Scholar]
  33. Khan A.I. Integrating blockchain technology into healthcare through an intelligent computing technique. Comput. Mater. Continua 2022 70 2 10.32604/cmc.2022.020342
    [Google Scholar]
  34. Housawi A.A. Lytras M.D. Digital transformation from a health professional practice and training perspective. Digital Transformation in Healthcare in Post-Covid-19 Times Academic Press 2023 193 204 10.1016/B978‑0‑323‑98353‑2.00011‑3
    [Google Scholar]
  35. Pagar K. Jain T. Kumar H. Bhardwaj A. Handa R. Medical Appliances energy consumption prediction using various machine learning algorithms. Blockchain and Deep Learning for Smart Healthcare WILEY 2024 10.1002/9781119792406.ch14
    [Google Scholar]
  36. Aldkheel A. Zhou L. Depression detection on social media: A classification framework and research challenges and opportunities. J. Healthc Inform. Res. 2024 8 1 88 120 10.1007/s41666‑023‑00152‑3 38273983
    [Google Scholar]
  37. Senan E.M. Al-Adhaileh M.H. Alsaade F.W. Aldhyani T.H.H. Alqarni A.A. Alsharif N. Uddin M.I. Alahmadi A.H. Jadhav M.E. Alzahrani M.Y. Diagnosis of chronic kidney disease using effective classification algorithms and recursive feature elimination techniques. J. Healthc Eng. 2021 2021 1 10 10.1155/2021/1004767 34211680
    [Google Scholar]
  38. Rehman A. Abbas S. Khan M.A. Ghazal T.M. Adnan K.M. Mosavi A. A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique. Comput. Biol. Med. 2022 150 106019 10.1016/j.compbiomed.2022.106019 36162198
    [Google Scholar]
  39. Mohanta B. Das P. Patnaik S. Healthcare 5.0: A paradigm shift in digital healthcare system using artificial intelligence, IOT and 5G communication. Bhubaneswar, India, 25-26 May 2019, pp. 191-196 International Conference on Applied Machine Learning (ICAML) 10.1109/ICAML48257.2019.00044
    [Google Scholar]
  40. Mbunge E. Muchemwa B. Jiyane S. Batani J. Sensors and healthcare 5.0: Transformative shift in virtual care through emerging digital health technologies. Global Health. J. 2021 5 4 169 177 10.1016/j.glohj.2021.11.008
    [Google Scholar]
  41. Bhavin M. Tanwar S. Sharma N. Tyagi S. Kumar N. Blockchain and quantum blind signature-based hybrid scheme for healthcare 5.0 applications. J. Inform. Security Applications 2021 56 102673 10.1016/j.jisa.2020.102673
    [Google Scholar]
  42. Du X. Chen B. Ma M. Zhang Y. Research on the application of blockchain in smart healthcare: Constructing a hierarchical framework. J. Healthc Eng. 2021 2021 1 13 10.1155/2021/6698122 33505644
    [Google Scholar]
  43. Ihnaini B. Khan M.A. Khan T.A. Abbas S. Daoud M.S. Ahmad M. Khan M.A. A smart healthcare recommendation system for multidisciplinary diabetes patients with data fusion based on deep ensemble learning. Comput. Intell. Neurosci. 2021 2021 1 4243700 10.1155/2021/4243700 34567101
    [Google Scholar]
  44. Nasr M. Islam M.M. Shehata S. Karray F. Quintana Y. Smart healthcare in the age of AI: Recent advances, challenges, and future prospects. IEEE Access 2021 9 145248 145270 10.1109/ACCESS.2021.3118960
    [Google Scholar]
  45. Khan M.F. Ghazal T.M. Said R.A. Fatima A. Abbas S. Khan M.A. Issa G.F. Ahmad M. Khan M.A. An iomt-enabled smart healthcare model to monitor elderly people using machine learning technique. Comput. Intell. Neurosci. 2021 2021 1 2487759 10.1155/2021/2487759 34868288
    [Google Scholar]
  46. Xu J. Glicksberg B.S. Su C. Walker P. Bian J. Wang F. Federated learning for healthcare informatics. J. Healthc Inform. Res. 2021 5 1 1 19 10.1007/s41666‑020‑00082‑4 33204939
    [Google Scholar]
  47. Li Y. Shan B. Li B. Liu X. Pu Y. Literature review on the applications of machine learning and blockchain technology in smart healthcare industry: A bibliometric analysis. J. Healthc Eng. 2021 2021 9739219 Aug 10.1155/2021/9739219 34426765
    [Google Scholar]
  48. Yamin Siddiqui S. Naseer I. Adnan Khan M. Faheem Mushtaq M. Ali Naqvi R. Hussain D. Haider A. Intelligent breast cancer prediction empowered with fusion and deep learning. Comput. Mater. Continua 2021 67 1 1033 1049 10.32604/cmc.2021.013952
    [Google Scholar]
  49. Dai W. Brisimi T.S. Adams W.G. Mela T. Saligrama V. Paschalidis I.C. Prediction of hospitalization due to heart diseases by supervised learning methods. Int. J. Med. Inform. 2015 84 3 189 197 10.1016/j.ijmedinf.2014.10.002 25497295
    [Google Scholar]
  50. Tariq A. Celi L.A. Newsome J.M. Purkayastha S. Bhatia N.K. Trivedi H. Gichoya J.W. Banerjee I. Patient-specific COVID-19 resource utilization prediction using fusion AI model. NPJ Digit. Med. 2021 4 1 94 10.1038/s41746‑021‑00461‑0 34083734
    [Google Scholar]
  51. Sedik A. Iliyasu A.M. Abd El-Rahiem B. Abdel Samea M.E. Abdel-Raheem A. Hammad M. Peng J. Abd El-Samie F.E. Abd El-Latif A.A. Deploying machine and deep learning models for efficient data-augmented detection of COVID-19 infections. Viruses 2020 12 7 769 10.3390/v12070769 32708803
    [Google Scholar]
  52. Qayyum J.Q. Abd El-Latif A.A. Collaborative federated learning for healthcare: Multi-modal covid-19 diagnosis at the edge. arXiv 2021 arXiv:2101.07511v1 10.48550/arXiv.2101.07511
    [Google Scholar]
  53. Brisimi T.S. Chen R. Mela T. Olshevsky A. Paschalidis I.C. Shi W. Federated learning of predictive models from federated Electronic Health Records. Int. J. Med. Inform. 2018 112 59 67 10.1016/j.ijmedinf.2018.01.007 29500022
    [Google Scholar]
  54. Medjahed H. Istrate D. Boudy J. Baldinger J.L. Dorizzi B. A pervasive multi-sensor data fusion for smart home healthcare monitoring. Taipei, Taiwan, 27-30 June 2011 , pp. 1466-1473 IEEE International Conference on Fuzzy Systems 10.1109/FUZZY.2011.6007636
    [Google Scholar]
  55. Jithish J. Alangot B. Mahalingam N. Yeo K.S. Distributed anomaly detection in smart grids: A federated learning-based approach. IEEE Access 2023 11 7157 7179 10.1109/ACCESS.2023.3237554
    [Google Scholar]
  56. Naeem A. Anees T. Naqvi R.A. Loh W.K. A comprehensive analysis of recent deep and federated-learning-based methodologies for brain tumor diagnosis. J. Pers Med. 2022 12 2 275 10.3390/jpm12020275 35207763
    [Google Scholar]
  57. Ng D. Lan X. Yao M.M.S. Chan W.P. Feng M. Federated learning: A collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets. Quant Imaging Med. Surg. 2021 11 2 852 857 10.21037/qims‑20‑595 33532283
    [Google Scholar]
  58. Ma Z. Zhang M. Liu J. Yang A. Li H. Wang J. Hua D. Li M. An assisted diagnosis model for cancer patients based on federated learning. Front Oncol. 2022 12 860532 10.3389/fonc.2022.860532 35311106
    [Google Scholar]
  59. Truhn D. Tayebi Arasteh S. Saldanha O.L. Müller-Franzes G. Khader F. Quirke P. West N.P. Gray R. Hutchins G.G.A. James J.A. Loughrey M.B. Salto-Tellez M. Brenner H. Brobeil A. Yuan T. Chang-Claude J. Hoffmeister M. Foersch S. Han T. Keil S. Schulze-Hagen M. Isfort P. Bruners P. Kaissis G. Kuhl C. Nebelung S. Kather J.N. Encrypted federated learning for secure decentralized collaboration in cancer image analysis. Med. Image Anal. 2024 92 103059 10.1016/j.media.2023.103059 38104402
    [Google Scholar]
  60. Rehman A. Athar A. Khan M.A. Abbas S. Fatima A. Atta-ur-Rahman Saeed A. Modelling, simulation, and optimization of diabetes type II prediction using deep extreme learning machine. J. Ambient Intell. Smart Environ. 2020 12 2 125 138 10.3233/AIS‑200554
    [Google Scholar]
  61. Tan Y.N. Tinh V.P. Lam P.D. Nam N.H. Khoa T.A. A transfer learning approach to breast cancer classification in a federated learning framework. IEEE Access 2023 11 27462 27476 10.1109/ACCESS.2023.3257562
    [Google Scholar]
  62. Yang Q. Yang J. Tan C. Shi H. Federated learning with privacy-preserving and model IP-right-protection. Mach Intell. Res. 2023 20 1 1 17 10.1007/s11633‑022‑1343‑2
    [Google Scholar]
  63. Su Z. Wang Y. Luan T.H. Zhang N. Li F. Chen T. Cao H. Secure and efficient federated learning for smart grid with edge-cloud collaboration. IEEE Trans Industr Inform. 2022 18 2 1333 1344 10.1109/TII.2021.3095506
    [Google Scholar]
  64. Ogier du Terrail J. Leopold A. Joly C. Béguier C. Andreux M. Maussion C. Schmauch B. Tramel E.W. Bendjebbar E. Zaslavskiy M. Wainrib G. Milder M. Gervasoni J. Guerin J. Durand T. Livartowski A. Moutet K. Gautier C. Djafar I. Moisson A.L. Marini C. Galtier M. Balazard F. Dubois R. Moreira J. Simon A. Drubay D. Lacroix-Triki M. Franchet C. Bataillon G. Heudel P.E. Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer. Nat. Med. 2023 29 1 135 146 10.1038/s41591‑022‑02155‑w 36658418
    [Google Scholar]
  65. Li Y. Wei X. Li Y. Dong Z. Shahidehpour M. Detection of false data injection attacks in smart grid: A secure federated deep learning approach. IEEE Trans Smart Grid 2022 13 6 4862 4872 10.1109/TSG.2022.3204796
    [Google Scholar]
  66. Wen M. Xie R. Lu K. Wang L. Zhang K. FedDetect: A novel privacy-preserving federated learning framework for energy theft detection in smart grid. IEEE Internet Things J. 2022 9 8 6069 6080 10.1109/JIOT.2021.3110784
    [Google Scholar]
  67. Zhang Y. Suleiman B. Alibasa M.J. Farid F. Privacy-aware anomaly detection in IoT environments using fedgroup: A group-based federated learning approach. J. Netw Syst. Manage 2024 32 1 20 10.1007/s10922‑023‑09782‑9
    [Google Scholar]
  68. Abbas S. Khan M.A. Al Ghamdi M.A. Kim S.-H. Bilal M. Masood K. Fused weighted federated deep extreme machine learning based on intelligent lung cancer disease prediction model for healthcare 5.0. Int. J. Intell. Syst. 2023 2023 2599161 10.1155/2023/2599161
    [Google Scholar]
  69. Singh P. Masud M. Hossain M.S. Kaur A. Muhammad G. Ghoneim A. Privacy-preserving serverless computing using federated learning for smart grids. IEEE Trans Industr Inform. 2022 18 11 7843 7852 10.1109/TII.2021.3126883
    [Google Scholar]
  70. Sakib S. Fouda M.M. Fadlullah Z.M. Nasser N. Alasmary W. A proof-of-concept of ultra-edge smart iot sensor: A continuous and lightweight arrhythmia monitoring approach. IEEE Access 2021 9 26093 26106 10.1109/ACCESS.2021.3056509
    [Google Scholar]
  71. Khan M.A. Abbas S. Rehman A. Saeed Y. Zeb A. Uddin M.I. Nasser N. Ali A. A machine learning approach for blockchain-based smart home networks security. IEEE Netw 2021 35 3 223 229 10.1109/MNET.011.2000514
    [Google Scholar]
  72. Haider A. Adnan Khan M. Rehman A. Rahman M.U. Seok Kim H. A real-time sequential deep extreme learning machine cybersecurity intrusion detection system. Comput. Mater. Continua 2021 66 2 1785 1798 10.32604/cmc.2020.013910
    [Google Scholar]
  73. Sheibani R. Nikookar E. Alavi S.E. An ensemble method for diagnosis of Parkinson’s disease based on voice measurements. J. Med. Signals Sens. 2019 9 4 221 226 10.4103/jmss.JMSS_57_18 31737550
    [Google Scholar]
  74. Tracy J.M. Özkanca Y. Atkins D.C. Hosseini Ghomi R. Investigating voice as a biomarker: Deep phenotyping methods for early detection of Parkinson’s disease. J. Biomed. Inform. 2020 104 103362 10.1016/j.jbi.2019.103362 31866434
    [Google Scholar]
  75. Sztaho D. Valalik I. Vicsi K. Parkinson’s disease severity estimation on hungarian speech using various speech tasks. Timișoara, Romania, 10-12 October 2019, pp. 1-6 10th International Conference on Speech. Technology and Human-Computer Dialogue (SpeD 2019) 2019 10.1109/SPED.2019.8906277
    [Google Scholar]
  76. Yaman O. Ertam F. Tuncer T. Automated Parkinson’s disease recognition based on statistical pooling method using acoustic features. Med. Hypotheses 2020 135 109483 10.1016/j.mehy.2019.109483 31954340
    [Google Scholar]
  77. Kuresan H. Samiappan D. Masunda S. Fusion of WPT and MFCC feature extraction in Parkinson’s disease diagnosis. Technol Health. Care 2019 27 4 363 372 10.3233/THC‑181306 30664511
    [Google Scholar]
/content/journals/cpb/10.2174/0113892010380715250728143022
Loading
Integrating IoMT and Federated Learning for Advanced Healthcare Monitoring in Healthcare 5.0
Bentham Science Publishers ; https://doi.org/10.2174/0113892010380715250728143022
/content/journals/cpb/10.2174/0113892010380715250728143022
/content/journals/cpb/10.2174/0113892010380715250728143022
Loading

Data & Media loading...


  • Article Type:     Research Article
Keywords: IoMT ; Healthcare 5.0 ; artificial intelligence ; Federated learning

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test