Parkinson Hastalığı İçin Öznitelik Seçiminin Önemi

Year 2020, Volume: 8 Issue: 1, 175 - 180, 28.01.2020

Kemal Akyol , Şafak Bayır , Baha Şen

https://doi.org/10.21541/apjes.541637

Cited By: 1

Abstract

Parkinson hastalığı, ileri yaşlarda ortaya çıkan nörolojik bir hastalıktır. Bu hastalık, en acı verici, tehlikeli ve tedavi edilmeyen hastalıklardan biridir. Bu çalışmada, bu hastalığın tespiti için sıralama tekniklerini kullanarak özniteliklerin önemliliğini değerlendirmeye dayalı yeni bir uygulama yapılmıştır. Parkinson hastalığı üzerinde özniteliklerin etkilerinin tanımlanması için Stability Selection (Kararlılık Seçimi) metodu uygulanmıştır. Bu hastalığın tanısında etkin olacak olan en iyi modeli elde etmek için seçilmiş öznitelikler veriseti ve tüm öznitelikler veriseti giriş verisi olarak Rastgele Orman ve Lojistik Regresyon algoritmalarına gönderilmiştir. En iyi performans sunan model bilgisini içeren bu çalışma bu hastalığın etkin teşhisi için güçlü bir araç olabilir.

Keywords

Parkinson hastalığı, özniteliğin önemi, Stability Selection, k-fold çapraz doğrulama, makine öğrenmesi

Importance of Attribute Selection for Parkinson Disease

Abstract

Parkinson
disease is a neurological disorder occurring at older ages. It is one of the
most painful, dangerous and untreated diseases. In this study, a new
application based on assessing the importance of attributes using the ranking
techniques was carried out for diagnosis of this disease. The effects of the
attributes on the Parkinson disease are determined by utilizing Stability
Selection method. The selected attributes dataset and all attributes dataset have
been sent as input data to the Random Forest and Logistic Regression algorithms
in order to investigate the best model which is to be effective in the
diagnosis of this disease. This study including the model which presented the
best performance might be a powerful tool for effective diagnosis of this
disease.

Keywords

Parkinson disease, importance of attribute, Stability Selection, k-fold cross validation, machine learning

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