Skip to main content
SpringerLink
Log in

Federated Learning of Explainable Artificial Intelligence Models for Predicting Parkinson’s Disease Progression

  • Conference paper
  • First Online:
Explainable Artificial Intelligence (xAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1901))

Included in the following conference series:

Abstract

Services based on Artificial Intelligence (AI) are becoming increasingly pervasive in our society. At the same time, however, we are also witnessing a growing awareness towards the ethical aspects and the trustworthiness of AI tools, especially in high stakes domains, such as the healthcare one. In this paper, we propose the adoption of AI techniques for predicting Parkinson’s Disease progression with the overarching aim of accommodating the urgent need for trustworthiness. We address two key requirements towards trustworthy AI, namely privacy preservation in learning AI models and their explainability. As for the former aspect, we consider the (rather common) case of medical data coming from different health institutions, assuming that they cannot be shared due to privacy concerns. To address this shortcoming, we leverage federated learning (FL) as a paradigm for collaborative model training among multiple parties without any disclosure of private raw data. As for the latter aspect, we focus on highly interpretable models, i.e., those for which humans are able to understand how decisions have been taken. An extensive experimental analysis carried out on a well-known Parkinson Telemonitoring dataset highlights how the proposed approach based on FL of fuzzy rule-based systems allows achieving, simultaneously, data privacy and interpretability. Results are reported for different data partitioning scenarios, also comparing the interpretable-by-design model with an opaque neural network model.

This work has been partly funded by the PNRR - M4C2 - Investimento 1.3, Partenariato Esteso PE00000013 - “FAIR - Future Artificial Intelligence Research” - Spoke 1 “Human-centered AI” and the PNRR “Tuscany Health Ecosystem” (THE) (Ecosistemi dell’Innovazione) - Spoke 6 - Precision Medicine & Personalized Healthcare (CUP I53C22000780001) under the NextGeneration EU programme, and by the Italian Ministry of University and Research (MUR) in the framework of the FoReLab and CrossLab projects (Departments of Excellence). This work was also partially supported by the project “SAFE: Studio e sviluppo di una piAttaForma per la prEvenzione degli infortuni lavorativi” funded by the University of Pisa under the call “PRA 2022–2023”.

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 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Ethics Guidelines for Trustworthy AI, Technical Report. European Commission. High Level Expert Group on AI (2019). https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai

  2. Cost of a Data Breach report. IBM (2022). https://www.ibm.com/account/reg/us-en/signup?formid=urx-51643

  3. Aledhari, M., Razzak, R., Parizi, R.M., Saeed, F.: Federated learning: a survey on enabling technologies, protocols, and applications. IEEE Access 8, 140699–140725 (2020)

    Article  Google Scholar 

  4. Barredo Arrieta, A., et al.: Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020)

    Article  Google Scholar 

  5. Chen, P., Du, X., Lu, Z., Wu, J., Hung, P.C.: EVFL: an explainable vertical federated learning for data-oriented Artificial Intelligence systems. J. Syst. Arch. 126, 102474 (2022). https://doi.org/10.1016/j.sysarc.2022.102474. https://www.sciencedirect.com/science/article/pii/S1383762122000583

  6. Corcuera Bárcena, J.L., et al.: Fed-XAI: federated learning of explainable artificial intelligence models. In: XAI.it 2022: 3rd Italian Workshop on Explainable Artificial Intelligence, Co-located with AI*IA 2022 (2022). https://ceur-ws.org/Vol-3277/paper8.pdf

  7. Corcuera Bárcena, J.L., Ducange, P., Ercolani, A., Marcelloni, F., Renda, A.: An approach to federated learning of explainable fuzzy regression models. In: 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8 (2022). https://doi.org/10.1109/FUZZ-IEEE55066.2022.9882881

  8. Daneault, J.F., Carignan, B., Sadikot, A.F., Duval, C.: Are quantitative and clinical measures of bradykinesia related in advanced Parkinson’s disease? J. Neurosci. Methods 219(2), 220–223 (2013). https://doi.org/10.1016/j.jneumeth.2013.08.009

    Article  Google Scholar 

  9. Niousha, D.K., Sert, O.C., Ozyer, T., Reda, A.: Fuzzy classification methods based diagnosis of Parkinson’s disease from speech test cases. Curr. Aging Sci. 12, 100–120 (2019). https://doi.org/10.2174/1874609812666190625140311

    Article  Google Scholar 

  10. Dipro, S.H., Islam, M., Al Nahian, A., Sharmita Azad, M., Chakrabarty, A., Reza, T.: A federated learning based privacy preserving approach for detecting Parkinson’s disease using deep learning. In: 2022 25th International Conference on Computer and Information Technology (ICCIT), pp. 139–144 (2022). https://doi.org/10.1109/ICCIT57492.2022.10055787

  11. Fiosina, J.: explainable federated learning for taxi travel time prediction. In: VEHITS (2021)

    Google Scholar 

  12. Fiosina, J.: Interpretable privacy-preserving collaborative deep learning for taxi trip duration forecasting. In: International Conference on Vehicle Technology and Intelligent Transport Systems, International Conference on Smart Cities and Green ICT Systems, pp. 392–411. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-17098-0_20

  13. Franciscatto, M.H., et al.: Towards a speech therapy support system based on phonological processes early detection. Comput. Speech Lang. 65, 101130 (2021). https://doi.org/10.1016/j.csl.2020.101130. https://www.sciencedirect.com/science/article/pii/S0885230820300632

  14. Gacto, M., Alcalá, R., Herrera, F.: Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures. Inf. Sci. 181(20), 4340–4360 (2011). https://doi.org/10.1016/j.ins.2011.02.021. https://www.sciencedirect.com/science/article/pii/S0020025511001034

  15. Grabczewski, K., Jankowski, N.: Feature selection with decision tree criterion. In: Fifth International Conference on Hybrid Intelligent Systems (HIS 2005), p. 6 (2005). https://doi.org/10.1109/ICHIS.2005.43

  16. Grover, S., Bhartia, S., Akshama, Yadav, A., K.R., S.: Predicting severity of Parkinson’s disease using deep learning. Procedia Comput. Sci. 132, 1788–1794 (2018). https://doi.org/10.1016/j.procs.2018.05.154. https://www.sciencedirect.com/science/article/pii/S1877050918308883

  17. Gunduz, H.: Deep learning-based Parkinson’s disease classification using vocal feature sets. IEEE Access 7, 115540–115551 (2019). https://doi.org/10.1109/ACCESS.2019.2936564

    Article  Google Scholar 

  18. Harel, B., Cannizzaro, M., Snyder, P.J.: Variability in fundamental frequency during speech in prodromal and incipient Parkinson’s disease: a longitudinal case study. Brain Cogn. 56(1), 24–29 (2004). https://doi.org/10.1016/j.bandc.2004.05.002. https://www.sciencedirect.com/science/article/pii/S0278262604001393

  19. Hlavica, J., Prauzek, M., Peterek, T., Musilek, P.: Assessment of Parkinson’s disease progression using neural network and ANFIS models. Neural Netw. World 26, 111–128 (2016). https://doi.org/10.14311/nnw.2016.26.006

  20. Jorge, J., et al.: Applying federated learning in the detection of freezing of gait in Parkinson’s disease. In: 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC), pp. 195–200 (2022). https://doi.org/10.1109/UCC56403.2022.00037

  21. Junaid, M., Ali, S., Eid, F., El-Sappagh, S., Abuhmed, T.: Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson’s disease. Comput. Methods Prog. Biomed. 234, 107495 (2023). https://doi.org/10.1016/j.cmpb.2023.107495. https://www.sciencedirect.com/science/article/pii/S016926072300161X

  22. Kairouz, P., et al.: Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019)

  23. Karan, B., Sahu, S.S., Mahto, K.: Parkinson disease prediction using intrinsic mode function based features from speech signal. Biocybern. Biomed. Eng. 40(1), 249–264 (2020). https://doi.org/10.1016/j.bbe.2019.05.005. https://www.sciencedirect.com/science/article/pii/S0208521618305564

  24. Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., He, B.: A survey on federated learning systems: vision, hype and reality for data privacy and protection. arXiv preprint arXiv:1907.09693 (2019)

  25. Lin, G., Wang, L., Marcogliese, P.C., Bellen, H.J.: Sphingolipids in the pathogenesis of Parkinson’s disease and parkinsonism. Trends Endocrinol. Metab. 30(2), 106–117 (2019). https://doi.org/10.1016/j.tem.2018.11.003. https://www.sciencedirect.com/science/article/pii/S1043276018302030

  26. Ludwig, H., et al.: IBM federated learning: an enterprise framework white paper, no. 1, p. 10 (2020). https://doi.org/10.48550/ARXIV.2007.10987. https://arxiv.org/abs/2007.10987

  27. Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)

    Google Scholar 

  28. Magesh, P., Myloth, R., Tom, R.: An explainable machine learning model for early detection of Parkinson’s disease using LIME on DaTSCAN imagery. Comput. Biol. Med. 126, 104041 (2020). https://doi.org/10.1016/j.compbiomed.2020.104041

    Article  Google Scholar 

  29. McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.Y.: Communication-efficient learning of deep networks from decentralized data. In: Singh, A., Zhu, J. (eds.) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 54, pp. 1273–1282. PMLR (2017)

    Google Scholar 

  30. Mothukuri, V., Parizi, R.M., Pouriyeh, S., Huang, Y., Dehghantanha, A., Srivastava, G.: A survey on security and privacy of federated learning. Future Gener. Comput. Syst. 115, 619–640 (2021)

    Article  Google Scholar 

  31. Nilashi, M., Ibrahim, O., Samad, S., Ahmadi, H., Shahmoradi, L., Akbari, E.: An analytical method for measuring the Parkinson’s disease progression: a case on a Parkinson’s telemonitoring dataset. Measurement 136, 545–557 (2019). https://doi.org/10.1016/j.measurement.2019.01.014. https://www.sciencedirect.com/science/article/pii/S0263224119300144

  32. Postuma, R., Montplaisir, J.: Predicting Parkinson’s disease - why, when, and how? Parkinsonism Relat. Disord. 15, S105–S109 (2009). https://doi.org/10.1016/S1353-8020(09)70793-X. https://www.sciencedirect.com/science/article/pii/S135380200970793X

  33. Renfroe, J., Bradley, M., Okun, M., Bowers, D.: Motivational engagement in Parkinson’s disease: preparation for motivated action. Int. J. Psychophysiol. 99, 24–32 (2016). https://doi.org/10.1016/j.ijpsycho.2015.11.014. https://www.sciencedirect.com/science/article/pii/S0167876015300490

  34. Shahid, A.H., Singh, M.P.: A deep learning approach for prediction of Parkinson’s disease progression. Biomed. Eng. Lett. 10, 227–239 (2020)

    Article  Google Scholar 

  35. Sonu, S.R., Prakash, V., Ranjan, R., Saritha, K.: Prediction of Parkinson’s disease using data mining. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 1082–1085 (2017). https://doi.org/10.1109/ICECDS.2017.8389605

  36. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 1, 116–132 (1985)

    Article  MATH  Google Scholar 

  37. Tsanas, A., Little, M.A., McSharry, P.E., Ramig, L.O.: Accurate telemonitoring of Parkinson’s disease progression by noninvasive speech tests. IEEE Trans. Biomed. Eng. 57(4), 884–893 (2010). https://doi.org/10.1109/TBME.2009.2036000

    Article  Google Scholar 

  38. Wang, G.: Interpret federated learning with shapley values. arXiv preprint arXiv:1905.04519 (2019)

  39. Wilbik, A., Grefen, P.: Towards a federated fuzzy learning system. In: 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6. IEEE (2021)

    Google Scholar 

  40. Wilcoxon, F.: Individual comparisons by ranking methods. In: Breakthroughs in Statistics, pp. 196–202. Springer, Heidelberg (1992). https://doi.org/10.1007/978-1-4612-4380-9_16

  41. Wu, Y., Cai, S., Xiao, X., Chen, G., Ooi, B.C.: Privacy preserving vertical federated learning for tree-based models. Proc. VLDB Endow. 13(12), 2090–2103 (2020). https://doi.org/10.14778/3407790.3407811

  42. Xue, Z., Zhang, T., Lin, L.: Progress prediction of Parkinson’s disease based on graph wavelet transform and attention weighted random forest. Expert Syst. Appl. 203, 117483 (2022). https://doi.org/10.1016/j.eswa.2022.117483. https://www.sciencedirect.com/science/article/pii/S0957417422008132

  43. Yadav, G., Kumar, Y., Sahoo, G.: Predication of Parkinson’s disease using data mining methods: a comparative analysis of tree, statistical and support vector machine classifiers. In: 2012 National Conference on Computing and Communication Systems, pp. 1–8 (2012). https://doi.org/10.1109/NCCCS.2012.6413034

  44. Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1–19 (2019)

    Article  Google Scholar 

  45. Zhu, X., Wang, D., Pedrycz, W., Li, Z.: Horizontal federated learning of Takagi-Sugeno fuzzy rule-based models. IEEE Trans. Fuzzy Syst. 30(9), 3537–3547 (2022). https://doi.org/10.1109/TFUZZ.2021.3118733

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122, Pisa, Italy

    José Luis Corcuera Bárcena, Pietro Ducange, Francesco Marcelloni, Alessandro Renda & Fabrizio Ruffini

Authors
  1. José Luis Corcuera Bárcena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pietro Ducange
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francesco Marcelloni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alessandro Renda
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fabrizio Ruffini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alessandro Renda .

Editor information

Editors and Affiliations

  1. Technological University Dublin, Dublin, Ireland

    Luca Longo

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bárcena, J.L.C., Ducange, P., Marcelloni, F., Renda, A., Ruffini, F. (2023). Federated Learning of Explainable Artificial Intelligence Models for Predicting Parkinson’s Disease Progression. In: Longo, L. (eds) Explainable Artificial Intelligence. xAI 2023. Communications in Computer and Information Science, vol 1901. Springer, Cham. https://doi.org/10.1007/978-3-031-44064-9_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-44064-9_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44063-2

  • Online ISBN: 978-3-031-44064-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation