Prediction of Parkinson’s Disease Using a Stack Ensemble Modelling

Abstract

Parkinson's disease is a prevalent neurodegenerative disorder with a significant impact on the quality of life of affected individuals. Early diagnosis and accurate prediction of disease progression are critical for timely intervention and personalized treatment plans. In this study, we propose an innovative machine learning approach utilizing a stack ensemble model for the prediction of Parkinson's disease. The stack ensemble model combines the predictive power of various machine learning algorithms, including decision trees, knn, naive bayes, and random forests to create a unified and robust predictive framework. Diverse datasets, encompassing clinical records, genetic information, and neuroimaging data, are meticulously processed and integrated to extract informative features. Through rigorous experimentation and validation on a comprehensive dataset, we demonstrate the superior predictive performance of the stack ensemble model when compared to individual algorithms. The model not only achieves higher accuracy but also offers enhanced interpretability by revealing the significant features contributing to Parkinson's disease prediction. This research underscores the potential of stack ensemble modelling as an effective tool in the early diagnosis and prognosis of Parkinson's disease, thereby facilitating tailored treatment plans and improved patient care. The findings of this study contribute to advancing our understanding of the disease and its predictive modelling in the realm of machine learning.