Machine Learning Models for Parkinson Disease: Systematic Review

Abstract

Background: With the increasing availability of data, computing resources, and easier-to-use software libraries, machine learning (ML) is increasingly utilized in disease detection and prediction, including Parkinson’s disease (PD). Despite the large number of studies published every year, very few ML systems have been adopted for real-world subject use. Objective: In particular, a lack of external validity may result in poor performance of these systems in clinical practice. Additional methodological issues in ML design and reporting can also hinder clinical adoption, even for applications that would benefit from such data-driven systems. To sample the current ML practices in PD applications, we conducted a systematic review of studies in 2020 and 2021 that use ML models to diagnose PD or to track PD progression. Methods: We conducted a systematic literature review in accordance with PRISMA in PubMed between January 2020 - April 2021, using the exact string “Parkinson’s” AND (“ML” OR “prediction” OR “classification” OR “detection” or “artificial intelligence” OR “AI”), resulting in 1085 publications from the search results. After a search query and review, we found 113 publications that used ML for the classification or regression-based prediction of PD or PD-related symptoms. Results: Only 25.7% of studies used a hold-out test set to avoid potentially inflated accuracies, and approximately half of the studies without a hold-out test set did not state this as a potential concern. Surprisingly, 38.9% of studies did not report on how or if models were tuned, and an additional 27.4% used ad hoc model tuning, which is generally frowned upon in ML model optimization. Only 15% of studies performed direct comparisons of results with other models, severely limiting the interpretation of results. Conclusions: This review highlights the notable limitations of current ML systems and techniques that may contribute to a gap between reported performance in research and the real-life applicability of ML models aiming to detect and predict diseases such as PD.