Application of Machine Learning and Data Analytics in Detection of Parkinsons Disease

Parkinson’s disease (PD) is a neurological disorder that causes increasing motor and non-motor symptoms. It affects millions of people around the world. Early detection and proper diagnosis are critical to successful management and intervention. This research investigates the important role of machine learning (ML) and data analytics techniques in the identification of Parkinson’s disease. The introduction emphasizes the need of rapid diagnosis, describes the limitations of traditional diagnostic procedures, and emphasizes the potential of machine learning and data analytics in tackling these difficulties. This study delves into the pivotal role of machine learning and data analytics in the early detection of Parkinson’s disease (PD). Employing a diverse array of methodologies including Logistic Regression, Decision Tree, KNN, SVM, Gaussian Naïve Bayes, Random Forest, AdaBoost, and XGB, we rigorously evaluated their performance in PD classification. The results unveil compelling findings, with Random Forest and XGB showcasing exceptional accuracies of $\mathbf{9 9 \%}$ and $100 \%$ respectively. These outcomes not only demonstrate the efficacy of advanced machine learning algorithms in PD diagnosis but also highlight the potential for early intervention and personalized treatment strategies. By harnessing the power of data analytics, this research underscores the transformative impact of technology in healthcare, particularly in disease detection and management. The insights gleaned from this study pave the way for the integration of machine learning methodologies into clinical practice, fostering improved patient care and outcomes for individuals afflicted with PD and other neurodegenerative disorders. Moreover, it sets a precedent for future research in leveraging artificial intelligence for enhanced healthcare delivery.