Diagnosis of neuro degenerative diseases using machine learning methods and wavelet transform Yapay ögrenme yöntemleri ve dalgacik dönüsümü kullanilarak nöro dejeneratif hastaliklarin teshisi
Journal of the Faculty of Engineering and Architecture of Gazi University, cilt.32, sa.3, ss.749-766, 2017 (SCI-Expanded, Scopus, TRDizin)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 32 Sayı: 3
- Basım Tarihi: 2017
- Dergi Adı: Journal of the Faculty of Engineering and Architecture of Gazi University
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
- Sayfa Sayıları: ss.749-766
- Anahtar Kelimeler: Boosting, Machine learning, Neuro-degenerative diseases, Radial basis function network, Wavelet transform
- Kırklareli Üniversitesi Adresli: Evet
Özet
This study suggests that the force signals applied to the ground may be used to classify neuro degenerative diseases (NDD) such as Amyotrophic lateral sclerosis (ALS), Huntington's disease (HD) and Parkinson's disease (PD). The experiments were performed using data with 16 control subjects (CO), 13 ALS, 20 HD and 15 PD. Firstly, the force signals were separated up to level 7 using Discrete Meyer (dmey) wavelet. Among the new signals, the approach signal at the seventh level was selected. The local maximums of the peaks, peak locations, peak widths and peak prominences were obtained by performing peak analysis on this signal. Then, 15 basic statistical features from each of these four peak features were obtained. Thus, 60 for each of left and right foot, 120 features were obtained. Among these 120 features, the ones giving the highest information were selected using OneRules classifier. Respectively, 93.1%, 97.22%, 83.87% and 92.18% accuracy was obtained on ALS CO, HD CO, PD CO and NDD CO datasets using Radial Basis Function Network (RBFNetwork), Adaptive Boosting (Adaboost) and Additive Logistic Regression (LogitBoost) algorithms.