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Parkinson's is a neurological nervous system disorder that affects movement. A disease that is closely related to nerve cells such as Parkinson's is likely to be detected using gait data. Nowadays, symptoms of many diseases start to appear at a very early age. For this reason, analysis studies performed according to age factor for all diseases have gained importance. Therefore, in this study, it was aimed to analyze the data obtained from the subjects according to the age factor by using Double Density 1-D Wavelet Transform (DD1DWT) and to detect Parkinson's disease (PD) with high accuracy. The data set consists of gait data from 15 subjects, young, adult and adult patients. Firstly, DD1DWT method was applied to data in three levels and, the approximation (CA) and detail (CD) coefficients were obtained. Afterwards, 10 features were extracted from the last level CA data obtained according to age factor. The extracted features were given to 4 different decision mechanisms in the form of binary classes as healthy young-adult patient and healthy adult-adult patient. The obtained results were interpreted with many statistical metrics. Significant results were obtained through expert systems and it was found that young-healthy data could be distinguished from adult-patient data with lower error values and 100% classification accuracy rate. ANN has proved its success for both classes with the error values closest to zero among the decision mechanisms. Although there are studies in this field in literature, the lack of adequate analysis of the effect of age factor on PH increased the importance of this study. Furthermore, the fact that some of the effective features used in this study were not used previously in this area where PD was automatically detected by expert systems supports the contribution of the study to the literature.

Çift Yoğunluklu 1-D Dalgacık Dönüşümü Kullanılarak Parkinson Hastalığının Yaş Faktörüne Göre Tespit Edilmesi