Detection of Parkinson’s Disease Based on Biological Features Using Deep Neural Models

The correct and timely finding of Parkinson’s disease (PD) is a very challenging issue because this disease is diagnosed when 60 to 80% of neurons get vanished. Basically, it arises due to the death of dopamine neurons of the substantia nigra. To avoid misdiagnose, this experimental study developed a deep learning-based model for detecting PD at its early stages. For classification, the various biological features have been taken based on CSF, urine, plasma, and serum. Also evaluated the performance of the proposed DNN model with other highly implemented supervised learning classifiers (SVM, Naïve Bayes, and extreme gradient boosting). The obtained results reveal that the developed DNN-based classifier gives better metric results as compared to other classifiers, i.e., 97.43%, 97.36%, 98.87%, and 98.18% accuracy, precision, recall, and F1-score, respectively. Henceforth, this developed DNN classifier may support practitioners or physicians to identify the disease in its early phases.