AN AUTOMATED PARKINSON’S DISEASE DIAGNOSIS: A FEATURE SELECTION APPROACH

Abstract

Parkinson’s disease is one of the neurodegenerative disorders that significantly affect human health. Patients experience various negative effects such as tremors, walking disorders, and impaired speech. The disease also causes instability in walking, leading to tremors, and affects their writing skills. Studies on detection of disease generally focus on speech analysis. However, PD can be diagnosed by exploiting the loss of motor ability. In this work, a data set which was recorded at Cerrahpasa Faculty of Medicine, Istanbul University is considered. The data were collected from 15 healthy subjects and 75 with Parkinson’s Disease.by a graphic tablet. Each subject asked to draw a spiral in two different conditions which are named as static spiral test (SST) and dynamic spiral test (DST) respectively, and the drawings transformed into X, Y and Z axis of movement, Grip Angle, and Pressure data. During the study, the effectiveness of SST and DST conditions are considered. Various machine learning algorithms have been tested to determine the best classifier. The effect of features was also considered by utilizing a feature elimination process. As a result, the best classification performance was obtained as 93.55% by using Kernel Naïve Bayes network with SST data, by omitting Z axis.

Keywords

• Artificial Intelligence
• Biomedical Computing
• Biomedical Signal
• Machine Learning
• Supervised Learning.

OTOMATİK PARKİNSON HASTALIĞI TEŞHİSİ: BİR ÖZELLİK SEÇİMİ YAKLAŞIMI

Abstract

Parkinson hastalığı, insan sağlığını önemli ölçüde etkileyen nörodejeneratif bozukluklardan biridir. Hastalar, titreme, yürüme bozuklukları ve konuşma bozuklukları gibi çeşitli olumsuz etkiler yaşarlar. Hastalık ayrıca yürüme dengesizliğine, titremelere ve yazma becerilerini etkiler. Hastalığın tespiti üzerine yapılan çalışmalar genellikle konuşma analizine odaklanmaktadır. Ancak, Parkinson hastalığı motor yetenek kaybını kullanarak teşhis edilebilir. Bu çalışmada, İstanbul Üniversitesi Cerrahpaşa Tıp Fakültesi'nde kaydedilen bir veri seti incelenmektedir. Veriler, 15 sağlıklı denekten ve Parkinson hastalığı olan 75 denekten bir grafik tableti kullanılarak toplandı. Her denekten, sırasıyla statik spiral testi (SST) ve dinamik spiral testi (DST) olarak adlandırılan iki farklı koşul altında bir spiral çizmesi istenmiş ve çizimler X, Y ve Z eksenlerine hareket, Kavrama Açısı ve Basınç verilerine dönüştürülmüştür. Çalışma sırasında, SST ve DST koşullarının etkinliği dikkate alınmıştır. En iyi sınıflandırıcıyı belirlemek için çeşitli makine öğrenimi algoritmaları test edilmiştir. Özelliklerin etkisi, bir özellik elemesi süreci kullanılarak da dikkate alınmıştır. Sonuç olarak, Z eksenini ihmal ederek SST verileri ile Kernel Naive Bayes ağı kullanılarak %93,55'lik en iyi sınıflandırma performansı elde edilmiştir.

Keywords

• Yapay Zeka
• Biyomedikal Hesaplama
• Biyomedikal Sinyal
• Makine Öğrenmesi
• Eğiticili Öğrenme.

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