A Survey of Machine Learning Methods for Diagnosing Parkinson's Disease with Handwriting

Parkinson's disease is a nervous system disorder for which early diagnosis is important. In recent years, promising results have been obtained for diagnosing Parkinson's disease with machine learning techniques. This review examines different studies on diagnosing Parkinson's disease using machine learning models and methods. Various methods, such as machine learning, deep learning, and transfer learning, classify Parkinson's disease. These models successfully distinguish Parkinson's patients from healthy individuals by using different data types, such as handwriting data and acoustic features. In this study, studies using handwriting for diagnosing Parkinson's were evaluated with a critical approach. It also informs the literature for future research in diagnosing Parkinson's disease using machine learning.