Prediction of dyskinesia in Parkinson’s disease patients using machine learning algorithms

Abstract

AbstractDyskinesias are non preventable abnormal involuntary movements that represent the main challenge of the long term treatment of Parkinson’s disease (PD) with the gold standard dopamine precursor levodopa. Applying machine learning techniques on the data extracted from the Parkinson’s Progression Marker Initiative (PPMI, Michael J. Fox Foundation), this study was aimed to identify PD patients who are at high risk of developing dyskinesias. Data regarding clinical, behavioral and neurological features from 697 PD patients were included in our study. Our results show that the Random Forest was the classifier with the best and most consistent performance, reaching an area under the receiver operating characteristic (ROC) curve of up to 91.8% with only seven features. Information regarding the severity of the symptoms, the semantic verbal fluency, and the levodopa treatment were the most important for the prediction, and were further used to create a Decision Tree, whose rules may guide the pharmacological management of PD symptoms. Our results contribute to the identification of PD patients who are prone to develop dyskinesia, and may be considered in future clinical trials aiming at developing new therapeutic approaches for PD.