Identification of Parkinson’s Disease Using Stacking Classifier

Abstract

Parkinson's disease, a neurodegenerative condition, impacts millions of individuals across the globe. Timely and precise identification is imperative for efficient administration and therapy. The current study centers around utilizing machine learning algorithms to forecast Parkinson's disease by analyzing biomedical and clinical information. A comprehensive dataset comprising demographic information, medical history, and clinical assessments is collected and preprocessed to handle missing values and ensure data quality. To determine the most significant factors that can predict the disease, methods for selecting and extracting features are employed. The effectiveness of stacking classifier algorithm is assessed in terms of their ability to make accurate predictions. The dataset is split into two parts, one for training and the other for testing. Cross-validation is used to adjust the model's hyperparameters and stay away from overfitting. The model has been thoroughly assessed using conventional classification metrics and support vector machines (SVM). The findings of this research indicate that machine learning has significant potential in accurately predicting Parkinson's disease. Consequently, healthcare providers have the ability to enhance the well-being of individuals with Parkinson's disease and detect those who may be at risk at an earlier stage of the condition. This study adds to the continuous attempts to make use of advanced data analysis methods for the prompt detection and treatment of neurodegenerative disorders such as Parkinson's.