Araştırma Makalesi

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

Cilt: 14 16 Eylül 2026
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Explainable Parkinson’s Disease Prediction from Keystroke Dynamics Using AdaBoost Ensemble Learning

Öz

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.

Anahtar Kelimeler

Kaynakça

  1. [1] Jankovic, J. (2008). Parkinson’s disease: clinical features and diagnosis. Journal of neurology, neurosurgery & psychiatry, 79(4), 368-376.
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  3. [3] Su, D., et al. (2025). Projections for prevalence of Parkinson’s disease and its driving factors in 195 countries and territories to 2050: modelling study of Global Burden of Disease Study 2021. bmj, 388.
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  5. [5] Postuma, R. B., et al. (2015). MDS clinical diagnostic criteria for Parkinson's disease. Movement disorders, 30(12), 1591-1601.
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  7. [7] Meral, M., Ozbilgin, F., & Durmus, F. (2025). Fine-Tuned Machine Learning Classifiers for Diagnosing Parkinson’s Disease Using Vocal Characteristics: A Comparative Analysis. Diagnostics, 15(5), 645.
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Ayrıntılar

Birincil Dil

İngilizce

Konular

Elektrik Mühendisliği (Diğer)

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

16 Eylül 2026

Gönderilme Tarihi

9 Mart 2026

Kabul Tarihi

13 Nisan 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 14

Kaynak Göster

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 (Eylül). https://doi.org/10.17694/bajece.1905893.
EndNote
Özbilgin F (01 Eylül 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, c. 14, Eyl. 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 (01 Eylül 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, c. 14, Eylül 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. 01 Eylül 2026;14. doi:10.17694/bajece.1905893

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