aceMachine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-<sup>123</sup>I-Ioflupane SPECT Imaging in a Real-World Parkinson's Disease Dataset
Background Parkinson's disease (PD) progression is highly heterogeneous, complicating clinical management and prognostication. While machine learning models have been developed using research datasets such as Parkinson's Precision Medicine Initiative (PPMI) and Parkinson's Disease Biomarkers Program (PDBP), their clinical translatability is limited due to differences in routinely collected data. The Hoehn and Yahr (H&Y) scale is commonly used in clinical practice to stage PD, yet most predictive models focus on…
Random Forest and Gradient Boosting models trained on routinely collected clinical records predicted Hoehn and Yahr scores 5 years after ioflupane SPECT imaging, with best accuracy using 2 years of follow-up data.
SPECT imaging features added limited prognostic value and implementing models on real-world data did not significantly close the gap between prognostic modeling and clinical implementation.
Clinical translatability of prior models is constrained by differences between research and routine data, and imaging added little prognostic value in this real-world implementation.
Evidence
- Peer-reviewedMovement Disorders Clinical Practice2026-07-25
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Truvace Impact Record TRV-2026-0574, v1: “Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-<sup>123</sup>I-Ioflupane SPECT Imaging in a Real-World Parkinson's Disease Dataset.” Truvace, 2026-07-27. /record/TRV-2026-0574 (accessed at citation time). sha256 57c70e0438bf7fc0…
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