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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 8 Sayı: 2

IDENTIFICATION of PARKINSON DISEASE THROUGH THE SPEECH SIGNALS BY USING THE MACHINE LEARNING TECHNIQUES

PARKİNSON HASTALIĞININ SES SİNYALLERİ ÜZERİNDEN MAKİNE ÖĞRENMESİ TEKNİKLERİ İLE TANIMLANMASI

Hasan Badem(KAHRAMANMARAŞ SÜTÇÜ İMAM ÜNİVERSİTESİ)0000-0002-4262-8774
Pages
630–637
Publication Date
2019-07-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In addition, the competitor methods have been also compared statistically.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:One of the most important symptoms of Parkinson's disease is speech disturbances.
📊
Key Findings & Data:In this study, it is aimed to investigate the performance of machine learning techniques including SVM, KNN, DT and NB in the classification of Parkinson's disease through speech signals.
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Conclusion & Impact:In addition, the competitor methods have been also compared statistically.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Parkinson hastalığı · makine öğrenmesi · sınıflandırma

Abstract

One of the most important symptoms of Parkinson's disease is speech disturbances. Thus, the disease can be classified by extracting the features from the speech signals that may remark the problem. Machine learning techniques produce very successful results in classification problems. In this study, it is aimed to investigate the performance of machine learning techniques including SVM, KNN, DT and NB in the classification of Parkinson's disease through speech signals. For this purpose, PDC data set with high dimensional features and instances has been used. In the performed experimental studies, high accuracy values have been obtained. In addition, the competitor methods have been also compared statistically.

Özet

Parkinson hastalığının en önemli belirtilerinden birisi konuşma bozukluklarıdır. Dolayısıyla, ses sinyallerinden problemi temsil edebilecek özniteliklerin çıkarılması ile hastalık sınıflandırılabilmektedir. Makine öğrenmesi teknikleri ile sınıflandırma problemlerinde oldukça başarılı sonuçlar üretmektedir. Bu çalışmada, Parkinson hastalığının ses sinyalleri üzerinden sınıflandırılmasında, KYK, ROS, DVM, NB ve KA makine öğrenmesi tekniklerinin başarımının araştırılması amaçlanmıştır. Bu amaç için literatüre yeni sunulan yüksek boyutlu öznitelik ve örnekleme sahip PDC veri seti kullanılmıştır. Gerçekleştirilen deneysel çalışmalarda, oldukça yüksek doğruluk değerleri elde edilmiştir. Ayrıca, kullanılan yöntemler istatiksel olarak karşılaştırılmıştır. Bu sonuçlara ek olarak, TBA ve DDA boyut indirme tekniklerinin başarıma etkileri analiz edilmiştir.

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Cite This Article

Badem, H. (2019). IDENTIFICATION of PARKINSON DISEASE THROUGH THE SPEECH SIGNALS BY USING THE MACHINE LEARNING TECHNIQUES. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 8 Sayı: 2, 630–637. https://doi.org/10.28948/ngumuh.524658

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