Making the Discrimination in the Walking Parameters of Individuals with Multiple Sclerosis and Parkinson's Disease with Machine Learning/Multipl Skleroz ve Parkinson Hastaligina Sahip Bireylerin Yurume Parametrelerindeki Ayrimin Makine Ogrenimi ile Yapilmasi

Objective: To determine the contribution of gait analysis to the differentiation and diagnosis of these diseases by examining the walking videos of individuals diagnosed with multiple sclerosis (MS) and Parkinson's disease (PD) using the deep learning method. Materials and Methods: A hybrid system based on Convolutional Neural Networks was developed for the detection of MS and PD based on gait analysis. The patients were walked on a flat surface of approximately 14 meters and video recordings were taken from the front, back and both sides during walking. Videos of a total of 28 patients, 12 PD and 16 MS patients, were used in the study. Results: In the study, the data was analyzed using machine learning techniques and the best accuracy score was obtained as 87.5%. Conclusion: The accuracy rate of machine learning models in the diagnosis, follow-up and treatment process of patients such as MS, PD and other neurological diseases has been examined and it has been concluded that it is inevitable that these methods will be used much more over time. Keywords: Machine learning, neurological diseases, gait analysis, kinetic analysis, kinematic analysis Amac: Derin ogrenme yontemi kullanilarak multipl skleroz (MS) ve Parkinson hastaligi (PH) tanisi alan bireylere ait yurume videolari incelenerek, yurume analizinin bu hastaliklarin birbirinden ayirimina ve taniya olan katkisinin belirlenmesidir. Gerec ve Yontem: Yurume analizine dayali MS ve PH'lerin tespiti icin Evrisimsel Sinir Aglarina dayali hibrit bir sistem gelistirilmistir. Hastalar yaklasik 14 metrelik duz bir zeminde yurutulmus ve yuruyus esnasinda on, arka ve her iki yanlardan video kayitlari alinmistir. Calismada 12 PH ve 16 MS hastasi olmak uzere toplam 28 hastaya ait videolar kullanilmistir. Bulgular: Calismada makine ogrenimi teknikleri kullanilarak veriler analiz edilmis ve en iyi dogruluk skoru %87,5 olarak elde edilmistir. Sonuc: MS, PH gibi hasta gruplarinda ve diger norolojik hastaliklarda hastalarinin tani, takip ve tedavi surecinde makine ogrenmesi modellerinin dogruluk orani incelenmis ve zamanla bu yontemlerden cok daha fazla yararlanilacaginin kacinilmaz oldugu gorusmustur. Anahtar Kelimeler: Makine ogrenimi, norolojik hastaliklar, yurume analizi, kinetik analiz, kinematik analiz