Neuro Degenerative Diseases Classification Using Triplelayer Feature Extraction Athisakthi A.1, Rani M. Pushpa2 1Department of Computer Science, Mother Teresa Women's University, Kodaikanal, India 2Proff & Head, Deapartment of Computer Science, Mother Teresa Women's University, Kodaikanal, India Online published on 31 March, 2020. Abstract Neuro Degenerative Diseases include Parkinson Diseases, Huntington Diseases, Ataxia, Myoclonus and Amyotrophic Lateral Sclerosis are medically, Hereditarily, pathologically fluctuated and are described by its symp-toms and execution of motor impairment. In this study, the data samples are of 15 subjects with PD, 20 subjects with HDdisease, 13 subjects with ALS and 16 subjects of healthy or fit persons. The database is composed a one minute recording of Force Sensitive Resistor (FSR) Signal. For feature deduction 2-level wavelet Decomposition is done using Discrete Wavelet Transform (DWT). The acquired features were evaluated using the means of 10-trials for fivefold cross-validation (FFCV) in LDA with a Random Forest classifier (RFC). For NDD localization. The Random Forest Classifier gives the better outcome contrast with SVM and QB ordinary classifiers. The experimental proves that the accuracy, sensitivity and the Specificity of the proposed system is highly accurate and efficient than the previous method. The percentage of the proposed method is 98.24%, 97.92% and 96.78% as each. Top Keywords Neuro Degenerative Diseases (NDD), Amyotrophic Lateral Sclerosis (ALS), Parkinson Disease (PD), Support Vector Machine (SVM), Huntington Disease (HD), Random Forest classifier (RFC), Discrete Wavelet Transform (DWT). Top |