Prediction of Parkinson Disease using Autoencoder Convolutional Neural Networks

Parkinson's Disease (PD) diagnosis is a challenging task for doctors because of the non-availability of separate testing and prediction methodology. PD is identified through various clinical tests, symptoms, and repeated clinical trials. Diagnosis of PD at an early stage is essential to improving patients' quality of life. An autoencoder feature extraction methodology is proposed for prediction of PD using Convolutional Neural Networks (CNNs). The feature extraction and de-noising of input data are performed using an autoencoder. CNN is used for classification and prediction. This algorithm has three layers: a convolutional layer, a pooling layer, and a dense layer. Feature extraction and image segmentation are automatically performed in the convolutional layer. A pooling layer performs downsizing. Finally, the dense layer is used for classification. The PPMI dataset is used for experimentation. The performance assessment is based on accuracy, precision, recall, and F1 measures.