Predictive value of machine learning in diagnosing cognitive impairment in patients with Parkinson's disease: a...

The current process used to diagnose cognitive impairment in patients with Parkinson's disease (PD) is unsatisfactory. More and more researchers had introduced machine learning into this field in recent years. This study explored the application of machine learning and its diagnostic performance in this field.

Since Parkinson's concurrent cognitive impairment is currently divided into different periods, most studies focus on the prodromal or early stages of Parkinson's cognitive impairment, and a few focuses on the dementia stage of Parkinson's. To ensure comprehensiveness, and model stability, we included patients with Parkinson's concurrent cognitive impairment in different periods who met the nadir criteria. A comprehensive literature search was carried out of the PubMed, Cochrane, Embase, and Web of Science databases. We used Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias for the machine learning models covered by the included original studies. The outcome indicators included the concordance-index (C-index), sensitivity, and specificity. A meta-analysis using the random-effects model was conducted to determine the C-index, and a double variable mixed-effects model was used to determine the sensitivity and specificity. The meta-analysis in this article was completed in STATA.

A total of 32 articles, comprising 10,778 patients and 51 prognostic models [summary c-statistic: 0.857, 95% confidence interval (CI) (0.842-0.873)], met the selection criteria and were included in this analysis. The total sensitivity and specificity of all models were 0.77 (95% CI: 0.72-0.81) and 0.83 (95% CI: 0.80-0.85), respectively, and those of the testing test were 0.85 (95% CI: 0.79-0.89), and 0.74 (95% CI: 0.70-0.78), respectively. A large part of the model showed a high risk of bias mainly because the study design was almost retrospective investigation.

This study constitutes a detailed mapping and assessment of the machine learning for prediction in PD patients with cognitive decline, which may provide stronger discriminative performance and can be used as a potential tool for early diagnosis.