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Journals / Avrupa Bilim ve Teknoloji Dergisi / 2021 / Sayı: 21

Detection of Parkinson's Disease Based on Deep Learning and Feature Selection

Özellik seçimi ve Derin Öğrenmeye Dayalı Parkinson Hastalığı Tespiti

Mehmet Bilal ER
Pages
428–436
Publication Date
2021-01-31
DOI
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AI Summary & Key Takeaways

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

The average accuracy was 89.67% without feature selection, and 94.23% on average with feature selection.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Parkinson's disease is a serious long-term neurodegenerative disease affecting the motor system.
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Key Findings & Data:In addition, ReliefF feature selection algorithm is used to compare the performance of genetic algorithm.
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Conclusion & Impact:The average accuracy was 89.67% without feature selection, and 94.23% on average with feature selection.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Parkinson · Derin öğrenme · Genetik algoritma

Abstract

Parkinson's disease is a serious long-term neurodegenerative disease affecting the motor system. It progresses slowly and causes the degeneration of its cells over time. This disease is difficult to diagnose and is one of the common diseases in society. Due to the deficiency of dopamine cells in the brain, it causes motor and non-motor (speech, smell) defects in the body. It is known that most patients with Parkinson's have voice disorders. Speech signals in Parkinson's patients differ greatly compared to normal people. In this study, a method based on deep learning by using the acoustic features of speech signals is proposed for the classification of Parkinson's disease. In the first step, acoustic features are passed through genetic algorithm and effective features are selected. In addition, ReliefF feature selection algorithm is used to compare the performance of genetic algorithm. These features are given as input to the Convolutional Neural Network (CNN) architecture designed in the second step. Experiments are done with a dataset widely used in the literature. This dataset consists of two classes and is taken from the UCI Machine Learning repository. The average accuracy was 89.67% without feature selection, and 94.23% on average with feature selection.

Özet

Parkinson hastalığı, motor sistemini etkileyen uzun süreli ciddi bir nörodejeneratif hastalıktır. Yavaşça ilerler ve zamanla hücrelerinin dejenerasyonuna neden olur. Bu hastalığın teşhisi zordur ve toplumdaki yaygın hastalıklardan biridir. Beyindeki dopamin hücrelerinin yetersizliği nedeniyle, vücutta motor ve motor olmayan (konuşma, koku alma) kusurlara yol açar. Parkinson hastalarının çoğunda ses bozukluğu olduğu bilenmektedir. Parkinson hastalarındaki konuşma sinyalleri, normal insanlara kıyasla büyük farklılıklar göstermektedir. Bu araştırmada, Parkinson hastalığının sınıflandırılması için konuşma sinyallerinin akustik özellikleri kullanılarak derin öğrenmeye dayalı yöntem önerilmektedir. İlk adımda akustik özellikler genetik algoritmadan geçirilerek etkin özellikler seçilmiştir. İkinci adımında tasarlanan Konvolüsyonel Sinir Ağı (KSA) mimarisine bu özellikler girdi olarak verilmiştir. Deneyler literatürde yaygın kullanılan veri seti ile yapılmıştır. Bu veri seti iki sınıftan oluşmaktadır ve UCI Makine Öğrenimi deposundan alınmıştır. Özellik seçimi olmadan ortalama %87,66, özellik seçim ile ise ortalama olarak %88,98 doğruluk elde edilmiştir.

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Cite This Article

ER, M. B. (2021). Detection of Parkinson's Disease Based on Deep Learning and Feature Selection. Avrupa Bilim ve Teknoloji Dergisi, Sayı: 21, 428–436. https://ajindex.com/articles/471228

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References (2)

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