Abstract / Summary
Background: /Objectives: Parkinson’s disease (PD) is a rapidly growing cause of neurological disability, yet diagnosis is often delayed in settings with limited access to movement-disorder specialists, including Kazakhstan and other Central Asian countries. This study aimed to develop a brief, self-administered, two-stage machine-learning-derived questionnaire based on binary symptom responses to identify individuals with possible clinically manifest PD who may benefit from specialist neurological assessment, and to evaluate its performance.
Methods: From outpatient records of 402 patients with confirmed PD, a preliminary 100-item yes/no questionnaire was compiled. In a development cohort of 389 participants aged 60 to 90 years (146 with PD, 243 controls) recruited in Almaty, Kazakhstan, we used ElasticNet logistic regression, random forest, and gradient boosting to select features, reducing the instrument to 35 items (15 in Stage 1, 20 in Stage 2). We assessed performance on a held-out test set (n=78) and in an interim prospective external-validation cohort (n=290) recruited independently, with neurologists blinded to questionnaire results.
Results: On the held-out test set, sensitivity was 86.2%, specificity 89.8%, accuracy 88.5%, and Stage 1 AUC 0.9557. In the interim external validation, sensitivity was 95.1%, specificity 94.4%, accuracy 94.5%, NPV 99.2%, and Stage 1 AUC 0.9962; only 12.1% of participants required Stage 2.
Conclusion: The two-stage questionnaire showed strong case-finding ability in identifying individuals who warrant neurological evaluation. Findings are interim; completion of external validation, larger geographically diverse cohorts, and comparison against existing instruments are needed before wider implementation.