Research Article

Explainable Parkinson’s Disease Prediction from Keystroke Dynamics Using AdaBoost Ensemble Learning

Volume: 14 September 16, 2026
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Explainable Parkinson’s Disease Prediction from Keystroke Dynamics Using AdaBoost Ensemble Learning

Abstract

Parkinson’s disease (PD) is a common, neurodegenerative disorder with a long asymptomatic period before the manifestation of its well-known motor symptoms and is also notoriously difficult to diagnose in its early stages. The present study is to use machine learning to achieve the diagnosis of Parkinson’s disease based on keystroke dynamics obtained while the users are typing on the keyboard. The Tappy Keystroke dataset in the PhysioNet dataset is utilized. 33 features from the hold time, flight time and latency were extracted from the samples through statistical analysis. The base classifier is the AdaBoost algorithm after combining with Bayesian optimization. The results compared with the Decision Tree, Naive Bayes, K-Nearest Neighbor, Artificial Neural Network, RUSBoost and Bagging all show that the AdaBoost has the highest accuracy of 87.16% and the AUC of 0.9129. It was observed that ensemble-based methods generally showed more balanced and higher performance compared to single classifiers. The decision process of the most successful model was interpreted using the SHAP method; it was determined that the HoldTime and FlightTime variation measures played a decisive role in classification. The results obtained indicate that keystroke dynamics can be used as a non-invasive, scalable PD biomarker that does not require a clinical setting.

Keywords

References

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Details

Primary Language

English

Subjects

Electrical Engineering (Other)

Journal Section

Research Article

Publication Date

September 16, 2026

Submission Date

March 9, 2026

Acceptance Date

April 13, 2026

Published in Issue

Year 2026 Volume: 14

APA
Özbilgin, F. (2026). Explainable Parkinson’s Disease Prediction from Keystroke Dynamics Using AdaBoost Ensemble Learning. Balkan Journal of Electrical and Computer Engineering, 14. https://doi.org/10.17694/bajece.1905893
AMA
1.Özbilgin F. Explainable Parkinson’s Disease Prediction from Keystroke Dynamics Using AdaBoost Ensemble Learning. Balkan Journal of Electrical and Computer Engineering. 2026;14. doi:10.17694/bajece.1905893
Chicago
Özbilgin, Ferdi. 2026. “Explainable Parkinson’s Disease Prediction from Keystroke Dynamics Using AdaBoost Ensemble Learning”. Balkan Journal of Electrical and Computer Engineering 14 (September). https://doi.org/10.17694/bajece.1905893.
EndNote
Özbilgin F (September 1, 2026) Explainable Parkinson’s Disease Prediction from Keystroke Dynamics Using AdaBoost Ensemble Learning. Balkan Journal of Electrical and Computer Engineering 14
IEEE
[1]F. Özbilgin, “Explainable Parkinson’s Disease Prediction from Keystroke Dynamics Using AdaBoost Ensemble Learning”, Balkan Journal of Electrical and Computer Engineering, vol. 14, Sept. 2026, doi: 10.17694/bajece.1905893.
ISNAD
Özbilgin, Ferdi. “Explainable Parkinson’s Disease Prediction from Keystroke Dynamics Using AdaBoost Ensemble Learning”. Balkan Journal of Electrical and Computer Engineering 14 (September 1, 2026). https://doi.org/10.17694/bajece.1905893.
JAMA
1.Özbilgin F. Explainable Parkinson’s Disease Prediction from Keystroke Dynamics Using AdaBoost Ensemble Learning. Balkan Journal of Electrical and Computer Engineering. 2026;14. doi:10.17694/bajece.1905893.
MLA
Özbilgin, Ferdi. “Explainable Parkinson’s Disease Prediction from Keystroke Dynamics Using AdaBoost Ensemble Learning”. Balkan Journal of Electrical and Computer Engineering, vol. 14, Sept. 2026, doi:10.17694/bajece.1905893.
Vancouver
1.Ferdi Özbilgin. Explainable Parkinson’s Disease Prediction from Keystroke Dynamics Using AdaBoost Ensemble Learning. Balkan Journal of Electrical and Computer Engineering. 2026 Sep. 1;14. doi:10.17694/bajece.1905893

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