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

Abayomi-Alli, Olusola O.; Damaševičius, Robertas; Maskeliūnas, Rytis; Abayomi-Alli, Adebayo

doi:10.15439/2020F188

Artykuł - szczegóły

Tytuł artykułu

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

Autorzy

Abayomi-Alli Olusola O. , Damaševičius Robertas , Maskeliūnas Rytis , Abayomi-Alli Adebayo

Wybrane pełne teksty z tego czasopisma

http://annals-csis.org

Identyfikatory

DOI

10.15439/2020F188

Warianty tytułu

Konferencja

Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)

Języki publikacji

Abstrakty

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.

Słowa kluczowe

speech impairment Parkinson voice analysis deep learning data augmentation interpolation small data

wady wymowy choroba Parkinsona analiza głosu głęboka nauka interpolacja

Wydawca

Polskie Towarzystwo Informatyczne

Czasopismo

Annals of Computer Science and Information Systems

Rocznik

2020

Tom

Vol. 21

Strony

371--380

Opis fizyczny

Bibliogr. 50 poz., wykr., tab., il.

Twórcy

autor

Abayomi-Alli Olusola O.

olusola.abayomi-alli@ktu.edu

Department of Software Engineering, Kaunas University of Technology, Kaunas, Lithuania

autor

Damaševičius Robertas

robertas.damasevicius@ktu.lt

Department of Software Engineering, Kaunas University of Technology, Kaunas, Lithuania

autor

Maskeliūnas Rytis

rytis.maskeliunas@vdu.lt

Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania

autor

Abayomi-Alli Adebayo

abayomiallia@funaab.edu.ng

Department of Computer Science, Federal University of Agriculture, Abeokuta, Nigeria

Bibliografia

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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

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