The performance of various machine learning methods for Parkinson's disease recognition: a systematic review

Parkinson's disease (PD) is a common neurodegenerative disorder that causes degeneration of dopaminergic neurons in the Nigrostriatal pathway and the discharge of Dopamine in the striatum. Machine learning algorithms have been used as a tool to predict and diagnose diseases. Some of these algorithms got the popularity due to their high recognition performance. In the recognition of PD, studies demonstrated various recognition performances and this systematic review study the performance of machine learning algorithms. This systematic review is based on the cochrane's proposed seven phases of review. After identifying the question of research and inclusion/exclusion criteria, we searched different related databases (SID, MagIran, PubMed, ProQuest, ScienceDirect, WoS, Scopus, and Google Scholar) with the help of combination of keywords. After selection of the studies we extract information and summarize the results. From 10,980 found-studies, and removing them based on inclusion/exclustion criteria, we selected 82 studies. To diagnose PD, 59 studies used clinical indicators, 2 studies used genetic characteristics, 12 used MRI, two used PET, 5 used SPECT and 2 used Laboratory markers. In most of these studies RF, SVM, LR have performed the best. The accuracies of RF, SVM, and LR are reported between 58.9%-99.42%, 65.2%-99.99%, and 43.9%-96%. The results show that the performance of RF, SVM, and LR are high for PD diagnosis. Therefore, they can be used for PD diagnosis as a help for doctors and specialist.