Machine learning‐based prediction of cognitive outcomes in de novo Parkinson's disease

Abstract

AbstractBackgroundCognitive impairment is a debilitating symptom in Parkinson’s disease (PD), with high variability in onset and course of progression. This present study aimed to establish an accurate multivariate machine learning model to predict cognitive outcome in newly diagnosed PD.MethodUsing baseline measures from the Parkinson’s Progression Markers Initiative (PPMI) cohort we subset clinical, biofluid and genetic/epigenetic variables. Annual cognitive assessments over an eight‐year time span were used to define two outcomes of i) dementia conversion, and ii) cognitive impairment. Outcomes and variable subsets were tested using multiple machine learning (ML) algorithms (random forest, elasticnet, SVM‐linear and cforest) to predict prognosis of individual cognitive decline in PD. Prediction was assessed using multiple measures including Area under the Curve (AUC) and Mathews Correlation Coefficient (MCC).ResultFor both cognitive outcomes, irrespective of the of ML algorithm, models consisting of clinical variables alone performed best, with high specificity and the largest AUC's (0.88 ‐ 0.92) and MCC's (0.57‐0.80). Notably, cognitive impairment outcome showed better sensitivity than dementia conversion outcome (0.72 – 0.81 vs 0.29 – 0.64, respectively). Addition of biological and genetic variables did not largely improve the model performance. However, a number of cerebrospinal fluid (CSF) proteins and epigenetic markers showed high predictive weighting in multiple models, when included alongside clinical variables.ConclusionMachine learning algorithms can accurately predict cognitive impairment development in PD. Within the generated models, clinical predictors appear to play a more prominent role than biological predictors. We further present additional future study data, expanding epigenetic research into PD dementia.