An Explainable Approach to Parkinson's Diagnosis Using the Contrastive Explanation Method-CEM

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dc.contributor.authorCicek, Ipek Balikci
dc.contributor.authorKucukakcali, Zeynep
dc.contributor.authorDeniz, Birgul
dc.contributor.authorAlgul, Fatma Ebru
dc.date.accessioned2026-04-04T13:31:09Z
dc.date.available2026-04-04T13:31:09Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractBackground/Objectives: Parkinson's disease (PD) is a progressive neurodegenerative disorder that requires early and accurate diagnosis. This study aimed to classify individuals with and without PD using volumetric brain MRI data and to improve model interpretability using explainable artificial intelligence (XAI) techniques. Methods: This retrospective study included 79 participants (39 PD patients, 40 controls) recruited at Inonu University Turgut Ozal Medical Center between 2013 and 2025. A deep neural network (DNN) was developed using a multilayer perceptron architecture with six hidden layers and ReLU activation functions. Seventeen volumetric brain features were used as the input. To ensure robust evaluation and prevent overfitting, a stratified five-fold cross-validation was applied, maintaining class balance in each fold. Model transparency was explored using two complementary XAI techniques: the Contrastive Explanation Method (CEM) and Local Interpretable Model-Agnostic Explanations (LIME). CEM highlights features that support or could alter the current classification, while LIME provides instance-based feature attributions. Results: The DNN model achieved high diagnostic performance with 94.1% accuracy, 98.3% specificity, 90.2% sensitivity, and an AUC of 0.97. The CEM analysis suggested that reduced hippocampal volume was a key contributor to PD classification (-0.156 PP), whereas higher volumes in the brainstem and hippocampus were associated with the control class (+0.035 and +0.150 PP, respectively). The LIME results aligned with these findings, revealing consistent feature importance (mean = 0.1945) and faithfulness (0.0269). Comparative analyses showed different volumetric patterns between groups and confirmed the DNN's superiority over conventional machine learning models such as SVM, logistic regression, KNN, and AdaBoost. Conclusions: This study demonstrates that a deep learning model, enhanced with CEM and LIME, can provide both high diagnostic accuracy and interpretable insights for PD classification, supporting the integration of explainable AI in clinical neuroimaging.
dc.identifier.doi10.3390/diagnostics15162069
dc.identifier.issn2075-4418
dc.identifier.issue16
dc.identifier.orcid0000-0001-7956-9272
dc.identifier.orcid0000-0002-3805-9214
dc.identifier.pmid40870922
dc.identifier.scopus2-s2.0-105015562133
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/diagnostics15162069
dc.identifier.urihttps://hdl.handle.net/11616/108615
dc.identifier.volume15
dc.identifier.wosWOS:001558464000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectParkinson's disease
dc.subjectexplainable artificial intelligence
dc.subjectcontrastive explanation method (CEM)
dc.subjectdeep learning
dc.subjectneuromorphological biomarkers
dc.titleAn Explainable Approach to Parkinson's Diagnosis Using the Contrastive Explanation Method-CEM
dc.typeArticle

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