Citation

Shuvo, Hasibul Islam and Rabbi, Gulam and Assaduzzaman, Md and Mahamud, Eram and Fahad, Nafiz and Liew, Tze Hui and Ohidujjaman, . (2026) Advancing early Parkinson's detection: A hybrid ensemble model with SHAP and LIME interpretability. Informatics in Medicine Unlocked, 65. p. 101801. ISSN 23529148

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Abstract

Early detection of Parkinson's disease remains difficult because symptoms overlap with related disorders, and there is no definitive diagnostic test. We present an interpretable end-to-end screening framework that unites a structured preprocessing protocol with a transparent stacking ensemble. Using a dataset of 2105 individuals with diverse demographic, lifestyle, and clinical profiles, we performed outlier detection and removal, class rebalancing with ADASYN, and Chi-Square feature selection to ensure high-quality inputs. Two high-performing gradient boosting classifiers, XGBoost and CatBoost, are optimized separately and then combined in a stacking architecture to leverage complementary strengths while reducing variance. Using an 80:20 stratified train–test split and stratified cross-validation, the stacked model achieved 97.15% accuracy, a .9806 AUC score, and a .9411 Cohen's Kappa Score and outperformed the individual baseline classifiers under the selected-feature evaluation setting. Importantly, the pipeline emphasizes interpretability without sacrificing performance. SHAP provides global attribution that highlights stable cohort-level prediction drivers, and LIME provides instance-specific explanations that clinicians can examine and interpret. Furthermore, the combined SHAP and LIME analyses highlighted UPDRS, MoCA, and Bradykinesia among the influential clinical features associated with diagnostic outcomes. The Mann–Whitney U test indicated statistically significant differences between the predicted score distributions of the two diagnostic classes (p < 0.05). The novelty of this work lies in three elements: a reproducible pipeline from preprocessing to prediction that couples rigorous outlier control, class rebalancing, and statistically grounded feature selection; a focused stacking design that uses two modern boosting learners to deliver dependable gains; and a clinician-oriented interpretability framework that provides both global and local explanations to support clinical decision-making. By bringing together data curation, stacked gradient boosting, and actionable explanations, this study offers a promising foundation for interpretable Parkinson's disease screening at the initial clinical assessment. However, prospective clinical validation is required before real-world deployment.

Item Type:	Article
Uncontrolled Keywords:	Parkinson's disease, Early diagnosis ML
Subjects:	R Medicine > R Medicine (General) > R858-859.7 Computer applications to medicine. Medical informatics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Sep 2026 07:44
Last Modified:	02 Sep 2026 07:44
URII:	http://shdl.mmu.edu.my/id/eprint/16542

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