Integrating Machine Learning Pipelines for Multimodal Biomarker Prediction in Alzheimer and Parkinson Disease: A Component of the Neurodiagnoses Framework
Osaghae, N. O.; GONZALEZ, M. M.
Show abstract
Alzheimers and Parkinsons 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, A{beta} 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.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Explainable artificial intelligence identifies an AQP4 polymorphism-based risk score associated with brain amyloid burden 95%
- Predicting future cognitive impairment in preclinical Alzheimer's disease using multimodal imaging: a multisite machine learning study 95%
- Integrating plasma, MRI, and cognitive biomarkers for personalized prediction of decline across cognitive domains 94%
Similar papers in this journal
- Temporal Modeling of Amyloid and Tau Trajectories in Alzheimer's Disease using PET and Plasma Biomarkers 95%
- Towards Harmonizing Quantification of Dopamine Neuron Imaging Biomarkers in Parkinson's Disease: The Centamine Scale 93%
- Progression of daily-life tremor measures in early Parkinson disease: a longitudinal continuous monitoring study 93%
Similar papers in this journal
- Using Machine Learning and Electronic Health Record (EHR) Data for the Early Prediction of Alzheimer’s Disease and Related Dementias 94%
- Use of Lecanemab and Donanemab in the Canadian Healthcare System: Evidence, Challenges, and Areas for Future Research 93%
- Real-world datasets for the International Registry for Alzheimer’s Disease and Other Dementias (InRAD) and other registries: an international consensus 93%
Similar papers in this journal
- Cognitive and Motor Correlates of Grey and White Matter Pathology in Parkinsons Disease 94%
- A neuroimaging measure to capture heterogeneous patterns of atrophy in Parkinson’s disease and dementia with Lewy bodies 94%
- MRI and cognitive scores complement each other to accurately predict Alzheimer’s dementia 2 to 7 years before clinical onset 94%
Similar papers in this journal
- c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer’s disease 95%
- Interpretable multivariate survival models: Improving predictions for conversion from mild cognitive impairment to Alzheimers disease (AD) via data fusion and machine learning 94%
- Examining heterogeneity in dementia using data-driven unsupervised clustering of cognitive profiles 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.