Enhancing Parkinson’s Disease Detection and Diagnosis: A Survey of Integrative Approaches Across Diverse Modalities

Abstract

Parkinson’s disease (PD) is a chronic neurodegenerative illness that affects the brain and central nervous system, leading to issues with pain, mobility, mood, and sleep. Early and precise diagnosis is vital for effective medical intervention and enhancing the quality level of life for patients. This review provides an extensive overview of current PD detection methods, emphasizing the integration of neuroimaging techniques, clinical data, and advanced computational algorithms. A wide range of neuroimaging modalities are examined by highlighting their roles in identifying structural and functional brain changes linked to PD. The review also explores the potential of datasets such as handwritten samples, Electroencephalography (EEG), Electrocardiography (ECG), voice recordings, gait analysis, and sensor data for PD detection. The review discusses various stages of data processing, including preprocessing, segmentation, and feature extraction, essential for improving the accuracy and efficiency of diagnostic models. The application of machine learning, deep learning, and transfer learning models for PD classification and prediction is reviewed, focusing on feature selection, model optimization, and the utilization of large, diverse datasets. AI systems can provide reliable and accurate Parkinson’s disease (PD) identification, enabling more efficient and prompt therapeutic treatments, by utilizing these sophisticated algorithms and a variety of datasets.

Keywords

• Parkinson’s disease
• dataset
• machine learning
• transfer learning
• segmentation