YAPAY ÖĞRENME YÖNTEMLERİ VE DALGACIK DÖNÜŞÜMÜ KULLANILARAK NÖRO DEJENERATİF HASTALIKLARIN TEŞHİSİ

Year 2017, Volume: 32 Issue: 3, 0 - 0, 07.09.2017

Fatih Aydın Zafer Aslan

https://doi.org/10.17341/gazimmfd.337621

Cited By: 4

Abstract

Bu çalışma da yere uygulanan kuvvet sinyalleri kullanılarak Amyotrophic Lateral Sclerosis (ALS), Huntington hastalığı (HH) ve Parkinson hastalığı (PH) gibi nöro‑dejeneratif hastalıkların (NDH) teşhisi ve sınıflandırılması gerçekleştirildi. Deneyler 16 kontrol bireyi (CO), 13 ALS, 20 HH ve 15 PH’ye ait veriler kullanılarak gerçekleştirildi. İlk olarak kuvvet sinyalleri, Discrete Meyer (dmey) dalgacığı kullanılarak yedinci seviyeye kadar ayrıştırıldı. Yeni oluşan sinyallerden yedinci seviyedeki yaklaşım sinyali seçildi. Bu sinyal üzerinde tepe (peak) analizi gerçekleştirilerek sinyalin lokal maksimumları, tepe’nin x‑ekseni değerleri, tepe genişliği ve tepe çıkıntıları elde edildi. Daha sonra bu dört tepe özelliğinin her birinden 15 adet temel istatistiksel özellik elde edildi. Böylelikle sol ayak için 60 ve sağ ayak için 60 olmak üzere toplamda 120 özellik elde edildi. Daha sonra OneRules sınıflandırıcı kullanılarak bu nitelikler içerisinden en çok enformasyon veren nitelikler seçildi. Bir sonraki aşamada ise RBFNetwork, Adaboost ve LogitBoost algoritmaları kullanılarak ALS‑CO, HH‑CO, PH‑CO ve NDH‑CO arasındaki ikili sınıflandırmalarda sırasıyla %93.1, %97.22, %83.87 ve %92.18 doğruluk sağlandı.

Keywords

Nöro dejeneratif hastalıklar, yapay öğrenme; dalgacık dönüşümü; RBFNetwork; Boosting

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