Alzheimer’s and Parkinson’s diseases are age-related neurodegenerative diseases that often require invasive procedures for diagnosis. Traditional diagnostic methods may fail to capture the interplay between genetic, molecular, and neuroanatomical markers. This manuscript aims to develop interpretable machine learning models that can predict key biomarkers, such as pTau, tTau, Aβ positivity, and motor symptom severity, using non-invasive data. Machine learning models (Random Forest, XGBoost) were trained using ADNI and PPMI baseline data. Using the APOE4 genotype, MRI volumes, cognitive scores, and demographics as inputs, SHAP was employed to enhance model interpretability. Models achieved AUCs of 0.859 (tTau) and 0.852 (pTau) with recall > 80%. The PD motor severity yielded an MAE of 5.72 and an R2 of 0.586. SHAP confirmed the contributions of APOE4 status, hippocampal atrophy, and dopaminergic asymmetries. The pipelines provide clinically meaningful predictions of biomarker status and motor symptoms, supporting interpretable, multi-axis neurodiagnostic tools within the neurodiagnoses framework.

The authors have declared no competing interest.

This study did not receive any funding

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The source data used in this study were openly available prior to the initiation of the research. Data were obtained from the Alzheimer Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), both of which require a data access request and approval before download. ADNI: Data can be accessed at https://adni.loni.usc.edu/data-samples/adni-data/ after completing the data access application process. PPMI: Data can be accessed at https://www.ppmi-info.org/access-data-specimens/download-data after completing the registration and data use agreement process. All datasets used in this study were downloaded from the above URLs following approval, and the versions correspond to the data available as of June 2025

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