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BiLSTM with Data Augmentation using Interpolation Methods to Improve Early Detection of Parkinson Disease

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
Języki publikacji
EN
Abstrakty
EN
The lack of dopamine in the human brain is the cause of Parkinson disease (PD) which is a degenerative disorder common globally to older citizens. However, late detection of this disease before the first clinical diagnosis has led to increased mortality rate. Research effort towards the early detection of PD has encountered challenges such as: small dataset size, class imbalance, overfitting, high false detection rate, model complexity, etc. This paper aims to improve early detection of PD using machine learning through data augmentation for very small datasets. We propose using Spline interpolation and Piecewise Cubic Hermite Interpolating Polynomial (Pchip) interpolation methods to generate synthetic data instances. We further investigate on reducing dimensionality of features for effective and real-time classification while considering computational complexity of implementation on real-life mobile phones. For classification we use Bidirectional LSTM (BiLSTM) deep learning network and compare the results with traditional machine learning algorithms like Support Vector Machine (SVM), Decision Tree, Logistic regression, KNN and Ensemble bagged tree. For experimental validation we use the Oxford Parkinson disease dataset with 195 data samples, which we have augmented with 571 synthetic data samples. The results for BiLSTM shows that even with a holdout of 90%, the model was still able to effectively recognize PD with an average accuracy for ten rounds experiment using 22 features as 82.86%, 97.1\%, and 96.37% for original, augmented (Spline) and augmented (Pchip) datasets, respectively. Our results show that proposed data augmentation schemes have significantly (p < 0.001) improved the accuracy of PD recognition on a small dataset using both classical machine learning models and BiLSTM.
Rocznik
Tom
Strony
371--380
Opis fizyczny
Bibliogr. 50 poz., wykr., tab., il.
Twórcy
  • Department of Software Engineering, Kaunas University of Technology, Kaunas, Lithuania
  • Department of Software Engineering, Kaunas University of Technology, Kaunas, Lithuania
  • Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania
  • Department of Computer Science, Federal University of Agriculture, Abeokuta, Nigeria
Bibliografia
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  • 3. Dorsey, E.R., Elbaz, A., Nichols, E., Abd-Allah, F., Abdelalim, A., Adsuar, J.C., Ansha, M.G., Brayne, C., Choi, J.Y.J., Collado-Mateo, D. and Dahodwala, N., 2018. Global, regional, and national burden of Parkinson's disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology, 17(11), 939-953. http://dx.doi.org/10.1016/S1474-4422(18)30295-3
  • 4. Saikia, A., Majhi, V., Hussain, M. and Paul, S., 2019. A Systematic review on Application based Parkinson’s disease Detection Systems. International Journal on Emerging Technologies 10(3): 166-173.
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Uwagi
1. Track 2: Computer Science & Systems
2. Technical Session: Advances in Computer Science & Systems
3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2425bf32-9cfe-4e3d-ac66-8d7bdd25f079
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