A retrospective analysis of patients from Shanghai Parkway Health Ophthalmology Department and Peking University People's Hospital Optometry Center. The dataset likely contains demographic and ocular biometric parameters used to develop a machine learning model for predicting myopia progression. The model was authored by Peng Zhou and last updated in April 2026.
Use Cases
- Building a predictive model for axial elongation rate based on pupil size and corneal thickness.
- Evaluating variable importance for myopia progression using biometric features like anterior chamber depth and corneal curvature.
- Comparing performance of machine learning models (e.g., XGBoost, random forest) on clinical biometric data.
- Assessing the clinical utility of ocular biometric parameters for early intervention planning.
Strengths
- Model performance metrics are reported, with XGBoost achieving R²=0.913 on the training set.
- Variable importance scores are provided, identifying pupil size (score 100) and corneal thickness (40.88) as key predictors.
- Data collection is described from two clinical centers, suggesting a multi-source validation approach.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- The dataset is 4.1 KB, indicating a very limited scope and likely small sample size.
Provenance
- Source
- Peng Zhou
- Collection Method
- Retrospective analysis of patient records from clinical centers.
- Freshness
- Last updated 2026-04-24 17:43:42; freshness should be verified.
- Geography
- China (Shanghai and Beijing)