Glaucoma shares similarities with neurodegenerative conditions like dementia, Parkinson's disease, and ischaemic optic neuropathy, which affect ocular health. However, current studies exclude neurodegenerative cases in neuropathy diagnosis and rely on 'black box' 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's, 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 years 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. Overall, machine outperformed clinicians, with 26.3% higher accuracy for neuropathy and 24.8% higher accuracy for glaucoma diagnosis. In conclusion, both non-explainable and explainable methods show promise in enhancing diagnostic support for clinical decision-making, with the privacy-preserving approach safeguarding data privacy.