Analysis of Different Modality of Data to Diagnose Parkinson's Disease Using Machine Learning and Deep Learning Approaches: A Review

Abstract

The dynamic nature of Parkinson's disease (PD) is that it gradually impacts regions of the brain that are responsible for the production of the dopamine hormone. Despite continuous efforts, no effective treatment or preventative approach exists for PD. Nonetheless, the disease can be detected. Our goal is to create a Machine Learning and Deep Learning-based system that can detect Parkinson's disease from a variety of data sources with high accuracy, sensitivity, specificity and interpretability. However, there have been significant advancements in the field of research, especially the use of artificial intelligence in the Parkinson's disease diagnostic process. We reviewed articles that were released between 2018 and 2024, concentrating on the most current studies that had been published. We chose 70 research articles for our review paper based on a set of criteria from a variety of online databases, including IEEExpress, medical databases like PubMed, Google Scholar, ResearchGate and ScienceDirect, and various publishers, including Elsevier, Taylor & Francis, Springer, MDPI, Plos One and so forth. According to our review, the majority of works make use of voice data. Our review study found that the highest accuracy level of most papers was above 90%, and the most commonly used algorithms were CNN and SVM. The main goal of this review study is to look into and put together information about the different ways that artificial intelligence, especially Machine Learning, can be used to find Parkinson's disease. Using diverse data gathered from multiple public and private datasets, we can infer that the application of artificial intelligence, particularly Machine Learning algorithms, for identifying Parkinson's disease plays a crucial role in the medical field.