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Ince-PD Model for Parkinson’s Disease Prediction Using MDS-UPDRS I & II and PDQ-8 Score

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Artificial Intelligence Applications and Innovations (AIAI 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 675))

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Abstract

Parkinson’s disease (PD) is one of the most prevalent and complex neurodegenerative disorders. Timely and accurate diagnosis is essential for the effectiveness of the initial treatment and improvement of the patients’ quality of life. Since PD is an incurable disease, the early intervention is important to delay the progression of symptoms and severity of the disease. This paper aims to present Ince-PD, a new, highly accurate model for PD prediction based on Inception architectures for time-series classification, using wearable data derived from IoT sensor-based recordings and surveys from the mPower dataset. The feature selection process was based on the clinical knowledge shared by the medical experts through the course of the EU funded project ALAMEDA. Τhe algorithm predicted total MDS-UPDRS I & II scores with a mean absolute error of 1.97 for time window and 2.27 for patient, as well as PDQ-8 scores with a mean absolute error of 2.17 for time window and 2.96 for patient. Our model demonstrates a more effective and accurate method to predict Parkinson Disease, when compared to some of the most significant deep learning algorithms in the literature.

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Notes

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Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No GA101017558.

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Authors and Affiliations

  1. Centre for Research & Technology Hellas, Information Technologies Institute, Marousi, Greece

    Nikos Tsolakis, Christoniki Maga-Nteve, Georgios Meditskos, Stefanos Vrochidis & Ioannis Kompatsiaris

  2. School of Informatics, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Georgios Meditskos

Authors
  1. Nikos Tsolakis
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  2. Christoniki Maga-Nteve
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  3. Georgios Meditskos
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  4. Stefanos Vrochidis
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  5. Ioannis Kompatsiaris
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Corresponding author

Correspondence to Nikos Tsolakis .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. University of Sunderland, Sunderland, UK

    John MacIntyre

  4. University of Leon, León, Spain

    Manuel Dominguez

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Tsolakis, N., Maga-Nteve, C., Meditskos, G., Vrochidis, S., Kompatsiaris, I. (2023). Ince-PD Model for Parkinson’s Disease Prediction Using MDS-UPDRS I & II and PDQ-8 Score. In: Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 675. Springer, Cham. https://doi.org/10.1007/978-3-031-34111-3_23

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  • DOI: https://doi.org/10.1007/978-3-031-34111-3_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34110-6

  • Online ISBN: 978-3-031-34111-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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