A Novel Ensemble Stacking Learning Algorithm for Parkinson's Disease Prediction

Parkinson's disease (PD) is a central sensory system illness that causes tremors and impairs mobility. Unfortunately, the identification of PD in the early stage is a challenging task. According to earlier studies, around 90 percent of persons with PD have some variety of vocal abnormality. A number of measures are available to identify Parkinson's disease. As a result, voice estimations can be utilized to identify the state of impacted persons. This research presents a novel method called as an ensemble stacking learning algorithm (ESLA), a classifier for identifying Parkinson's disease on the collected data set from the healthy people. To calculate the performance, the proposed ensemble method is compared with other existing techniques and finds the improved classification ability of this proposed method. It is exhibited that the proposed method for PD patients creates the most reliable outcomes and accomplishes the highest accuracy. This ensemble approach is implemented as different stacked models using various classifiers such as random forest (RF), XGBoost (extreme gradient boost), linear regression, AdaBoost (AB), and multilayer perceptron (MLP), and an analysis on the level of accuracy achieved through prediction is done. Finally, the performance of the proposed method among the chosen algorithms is suggested for the prediction of disease compared with other algorithms.

Automated summary

This research introduces a novel ensemble learning algorithm to identify Parkinson's disease using voice estimations. The method demonstrates improved classification ability compared to other techniques, providing more reliable outcomes and higher accuracy for Parkinson's disease patients.