Genetic Prediction of Parkinson’s Diagnosis: Firth to Ensemble Learning

By utilizing a targeted genetic assay within a Fox Insight cohort ( N = 1,987), this research establishes a hybrid, transparent, and interpretable predictive framework. Initial modeling via Firth penalized logistic regression discovered enrichment regarding the GBA N370S locus, highlighting the critical role of epidemiological evaluation in human study populations. Advanced ensemble learning methods, refined through a meta-learner gradient boosting machine, attained an out-of-sample AUC of 0.929 and MCC of 0.743 on 15% of the analysis dataset partitioned via random sampling and strictly held-out from model training. Both global, visual machine learning explanations and local-Shapley interpretations provide transparency into the models and individual predictions representative of practical, collaborative human-artificial intelligence efforts, offering a solution that supports classification while remaining accessible and economical.