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However, current studies exclude neurodegenerative cases in neuropathy diagnosis and rely on \u2018black box\u2019 models. To address this, we applied explainable machine learning to optical coherence tomography (OCT) data, integrating a privacy-preserving mechanism to create a reliable neuropathy diagnostic tool. OCT data from 268 glaucomatous, 334 normal, 56 dementia, 60 Parkinson\u2019s, and 93 ION eyes were analysed from a Sydney-based eye clinic. Spatial and frequency domain features were extracted, followed by feature selection and hierarchical classification. Model interpretability was enhanced using SHapley Additive exPlanations and partial dependency analysis, and privacy was preserved incorporating a differential privacy mechanism. A team of three clinicians, with 12, 11, and 6\u00a0years of experience, evaluated their performance on the same dataset for a human versus machine comparison, with the machine achieving an area under the curve of 0.90 for classifying neuropathy. 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