Many debilitating, fatal disorders, like Parkinson's disease, which are becoming more common, exhibit neurodegeneration. Development of unique, more potent medicinal approaches is crucial in order to fight these deadly diseases. Commercial gait detection systems based on force plates and footprints have been effectively used in the clinical diagnosis of such diseases. The classification of model for old versus young subjects with and without neurodegenerative diseases was constructed using MATLAB software. The analysis with respect to stride interval is mentioned in this paper. The 15 subjects were taken for this experiment; among these, five healthy young as well as old individuals were considered and five older adults with Parkinson’s disease were taken. Therefore, the two classes were formed using SVM kernel modeling for the diseased and healthy subjects. The classification for stride length interval (0.95–1.5 s) for SVM modeling was providing 96.7% validation accuracy.