An ensemble nearest neighbor boosting technique for prediction of Parkinson’s disease

Abstract

Parkinson’s disease (PD) is a complex condition that affects an individual’s motor features and intensifies over time. Symptoms vary between individuals and can go undetected at an early stage. Voice data has shown potential in predicting PD. In recent years, Machine Learning (ML) has made significant progress, with ensemble ML approaches proving to be effective in various applications. This research aims to detect PD at an early stage using ensemble techniques on the Parkinson’s speech dataset (PSV), which contains several sound recordings with 27 input features and one target feature. Feature importance was computed using Mean Decrease in Impurity (F-MDI), Feature Permutation (F-PER), and Pearson’s Correlation (F-CORR) techniques. Several supervised ML baseline models were compared with the proposed Nearest Neighbor Boosting (NNB) technique, which combines k-Nearest Neighbor (k-NN) and Gradient Boosting (GB). The NNB technique outperformed the baseline models for multiple performance metrics, including accuracy, recall, precision, F-score, the area under the curve (AUC), receiver operating characteristic (ROC), and confusion matrix (CM). The findings suggest that the designed ensemble method can be a promising method for PD detection.

Keywords

• Ensemble methods
• Machine learning
• Nearest neighbor
• Boosting
• Parkinson’s disease
• Prediction