Abstract
Accurately detecting Parkinson's disease (PD) at an early stage is certainly indispensable for slowing down its progress and providing patients the possibility of accessing to disease-modifying therapy. Towards this end, the premotor stage in PD should be carefully monitored. An innovative deep-learning technique is introduced to early uncover whether an individual is affected with PD or not based on premotor features. Specifically, to uncover PD at an early stage, several indicators have been considered in this study, including Rapid Eye Movement and olfactory loss, Cerebrospinal fluid data, and dopaminergic imaging markers. A comparison between the proposed deep learning model and twelve machine learning and ensemble learning methods based on relatively small data including 183 healthy individuals and 401 early PD patients shows the superior detection performance of the designed model, which achieves the highest accuracy, 96.45% on average. Besides detecting the PD, we also provide the feature importance on the PD detection process based on the Boosting method.
Highlights
Parkinson’s disease (PD) is becoming an important degenerative disease of the central nervous system, affecting the quality of lives of millions of seniors worldwide [1]
Results showed that the designed deep learning offers superior detection performance compared to the twelve considered machine learning models in discriminating normal people with patients who have Parkinson’s disease
The early detection of PD is essential to a better understanding of the disease causes, initiate therapeutic interventions, and enable developing appropriate treatments
Summary
Parkinson’s disease (PD) is becoming an important degenerative disease of the central nervous system, affecting the quality of lives of millions of seniors worldwide [1]. In [6], three common machine learning algorithms, namely Random Forest (RF) or Support Vector Machine (SVM) and neural network, have been applied to detect Parkinson’s disease based on acoustic analysis of speech It has been shown the promising results of RF an SVM in early PD detection. The used PD data from the Parkinson’s Progression Markers Initiative (PPMI) is relatively small and includes features from 183 healthy individuals and 401 early PD patients, which may make the application the machine learning methods attractive to investigate under this small dataset problem. Results showed that the designed deep learning offers superior detection performance compared to the twelve considered machine learning models in discriminating normal people with patients who have Parkinson’s disease.
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