Detection of Parkinson's Disease by Using Machine Learning Stacking and Ensemble Method

Abstract

Using speech data, it is difficult to learn through machine learning how to diagnose Parkinson's disease (PD) and evaluate the effects of treatment. For this issue, the article has developed a three-stage PD discovery method. The base classifiers used in the initial stage are logistic regression (LR), K-nearest neighbor (KNN), naive bayes (NB), support vector machine (SVC), and decision tree (DT). The second stage, or stack model, is a meta-model that combines all of the classifiers mentioned earlier. The third stage ensemble model consists of Bagging, AdaBoost, Random Forest (RF), and Gradient Boosting (GBC) components. The RF and GBC classifiers are utilized to estimate the most important features from the PD dataset. The models' validation has been evaluated using the confusion matrix and validation metrics like precision, recall, and F1 score. Out of all the ensemble models, the GBC—the third model—had the highest accuracy with testing data—97.43%. KNN from the base model and stacking from the meta-model, on the other hand, had the highest accuracy, with 94.87% each. Out of all the models mentioned in this manuscript, the GBC is the only ensemble model classifier with the highest accuracy. The proposed classifier appears to be an extremely useful model for the discovery of Parkinson's disease, as demonstrated by the exploratory findings and factual analyses.