An ensemble technique to predict Parkinson's disease using machine learning algorithms

Parkinson's Disease (PD) is a progressive neurodegenerative disorder affecting motor and non-motor symptoms. Its symptoms develop slowly, making early identification difficult. Machine learning has a significant potential to predict Parkinson's disease on features hidden in voice data. This work aimed to identify the most relevant features from a high-dimensional dataset, which helps accurately classify Parkinson's Disease with less computation time. Three individual datasets with various medical features based on voice have been analyzed in this work. An Ensemble Feature Selection Algorithm (EFSA) technique based on filter, wrapper, and embedding algorithms that pick highly relevant features for identifying Parkinson's Disease is proposed, and the same has been validated on three different datasets based on voice. These techniques can shorten training time to improve model accuracy and minimize overfitting. We utilized different ML models such as K-Nearest Neighbors (KNN), Random Forest, Decision Tree, Support Vector Machine (SVM), Bagging Classifier, Multi-Layer Perceptron (MLP) Classifier, and Gradient Boosting. Each of these models was fine-tuned to ensure optimal performance within our specific context. Moreover, in addition to these established classifiers, we proposed an ensemble classifier is found on a high optimal majority of the votes. Dataset-I achieves classification accuracy with 97.6 %, F1-score 97.9 %, precision with 98 % and recall with 98 %. Dataset-II achieves classification accuracy 90.2 %, F1-score 90.2 %, precision 90.2 %, and recall 90.5 %. Dataset-III achieves 83.3 % accuracy, F1-score 83.3 %, precision 83.5 % and recall 83.3 %. These results have been taken using 13 out of 23, 45 out of 754, and 17 out of 46 features from respective datasets. The proposed EFSA model has performed with higher accuracy and is more efficient than other models for each dataset.