Explainable machine learning-driven models for predicting Parkinson’s disease and its prognosis: obesity patterns associations and models development using NHANES 1999–2018 data

Figures

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  Fig. 1. Participants filtering workflow and conceptual architecture of this study

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  Fig. 2. Correlation heatmaps of Kendall (A), Pearson (B), Spearman (C), and MIC (D) correlation coefficient algorithms. MIC, Maximum Information Coefficient; PIR, poverty-to-income ratio; TC, total cholesterol; HDL, high-density lipoprotein; HbA1c, glycated hemoglobin; BUN, blood urea nitrogen; ALT, alanine transaminase; AST, aspartate aminotransferase; CVD, cardiovascular disease; DM, diabetes mellitus

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  Fig. 3. Subgroup analyses forest plots of for association estimates: between compound obesity and PD (A) and compound obesity and all-cause mortality (B). HbA1c, glycated hemoglobin; DM, diabetes mellitus; OR, odds ratio; HR, hazard ratio; 95%CI, 95% confidence interval

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  Fig. 4. Multiple imputations forest plots for association estimates: between general obesity and PD (A), abdominal obesity and PD (B), compound obesity and PD (C), general obesity and all-cause mortality (D), abdominal obesity and all-cause mortality (E), and compound obesity and all-cause mortality (F). OR, odds ratio; HR, hazard ratio; 95%CI, 95% confidence interval

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  Fig. 5. Restricted cubic spline plots with four knots of BMI-PD (A), WC-PD (B), BMI-all-cause mortality (C), and WC-all-cause mortality (D) associations. The red line represented the odds ratios, while the blue line denoted the 95% confidence intervals. BMI, body mass index; WC, waist circumference

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  Fig. 6. The generation and predictive value of key features for PD. C-index of 76 algorithm combinations models (A). Nomogram of PD predictive model (B). ROC (C) and calibration (D) curves for PD predictive model in SMOTE set. ROC (E) and calibration (F) curves for PD predictive model in test set. HDL, high-density lipoprotein; BUN, blood urea nitrogen; AST, aspartate aminotransferase; PD, Parkinson's disease; ROC, receiver operating characteristic; AUC: area under the ROC curve; LASSO, least absolute shrinkage and selection operator; RF, random forest; SMOTE, Synthetic Minority Oversampling Technique

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  Fig. 7. The prognostic value of key features for PD. Nomogram of PD prognostic model (A). Time-point ROC (B) and calibration (C) curves for PD prognostic model in train set. Time-point ROC (D) and calibration (E) curves for PD prognostic model in test set. HDL, high-density lipoprotein; BUN, blood urea nitrogen; AST, aspartate aminotransferase; PD, Parkinson's disease; ROC, receiver operating characteristic; AUC: area under the ROC curve

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  Fig. 8. Kaplan–Meier curves for PD across different obesity patterns. Kaplan–Meier curve (A) and risk table (B) in train set. Kaplan–Meier curve (C) and risk table (D) in test set. KM, Kaplan–Meier

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  Fig. 9. SHAP analysis for LASSO + RF model. SHAP summary plot (A) and interaction plot (B) of PD predictive model. SHAP force plots of participants without PD (C) and with PD (D). SHAP summary plot (E) and interaction plot (F) of PD prognostic model. SHAP force plots of PD participants with survival (G) and with death (H). HDL, high-density lipoprotein; BUN, blood urea nitrogen; AST, aspartate aminotransferase; PD, Parkinson's disease; LASSO, least absolute shrinkage and selection operator; RF, random forest; SHAP, Shapley additive explanations

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  Fig. 10. Feature importance analyses for PD predictive (A) and prognostic (B) models. HDL, high-density lipoprotein; BUN, blood urea nitrogen; AST, aspartate aminotransferase; PD, Parkinson's disease; SHAP, Shapley additive explanations