A Comparative Analysis of Advanced Machine Learning Algorithms to diagnose Parkinson's Disease

Abstract

Parkinson's disease (PD) is a prevalent and debilitating neurological disorder affecting millions worldwide, necessitating early and precise diagnosis for effective management and therapy. Recent breakthroughs in artificial intelligence (AI) applications within the medical field have showcased promising potential in disease identification and diagnosis. This study presents a comprehensive analysis of cutting-edge AI-based methods for the identification of Parkinson's disease. This paper encompasses extensive testing on substantial datasets containing both PD patients and healthy individuals, aiming to assess the efficacy of AI models. Performance metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC-ROC) are employed to evaluate model performance rigorously. This paper is not only reviewing the state-of-the-art AI techniques for Parkinson's disease identification but also introduces novel contributions in the field. Our results demonstrate the potential of AI in achieving early and accurate PD diagnosis, offering hope for improved patient care and treatment outcomes. Furthermore, this study paves the way for future research and development in the domain of AI-driven healthcare, with the ultimate goal of enhancing the quality of life for individuals suffering from Parkinson's disease and similar neurodegenerative conditions.