Parkinson's Disease Classification through Gait Analysis: Comparative study of deep learning and machine learning algorithms

Parkinson's disease (PD) is a neurodegenerative disorder that affects millions of people worldwide, causing various motor and non-motor symptoms. Early diagnosis of PD is crucial for timely intervention and management. Gait analysis provides insights into the motor impairments with PD, aiding in early detection. In this study, different deep learning models such as CNN, LSTM, and CNN-LSTM with varying neural network depths were explored to classify PD using gait data acquired through sensor technology. The study then compared the results of deep learning models with machine learning algorithms (Random Forest (RF) and Decision Trees (DT)). The dataset used in this study consists of 93 persons with PD and 73 healthy controls (HC) collected through sensor technology. The findings reveal that the RF algorithm achieved the highest accuracy of 96%, followed by the CNN-LSTM model of 95.49 %.