{"doi":"10.1002/dad2.70161","title":"Multi‐modal machine learning for predicting amyloid positivity using on‐ramp driving","abstract":"INTRODUCTION: Early detection of amyloid p is critical for Alzheimer's disease (AD) risk identification. This study leverages machine learning of multi-modal attributes, including vehicular, physiological, and demographic data, to classify older adults with and without amyloid positivity. METHODS: 0.05) were used to train random forest and XGBoost classifiers to classify amyloid-positive and -negative participants, with feature importance evaluated based on model performance. RESULTS: Integrating multiple data modalities (demographics, vehicular, and physiological features) improved classification performance, distinguishing amyloid status. XGBoost with all statistically significant features achieved the highest accuracy (85.1%). Vehicular data provided the most predictive power, highlighting driving behavior relevance for classification. DISCUSSION: Results underscore the importance of complementary insights from on-ramp multi-modal data to predict amyloid status and potential early AD detection. Highlights: We analyzed driving behavior and physiological signals for cognitive decline detection.Artificial intelligence (AI) models (random forest, XGBoost) effectively classified amyloid beta positive and negative participants.Interpretable AI identified on-ramp driving, that is, ZOI_1, as key for classification.Multi-modal analysis during on-ramp driving aids early cognitive decline detection.Challenging traffic environments enable non-invasive cognitive health monitoring.","journal":"Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring","year":2025,"id":536501,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.943,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1421311,"name":"Yi Lu Murphey","orcid":"0000-0002-0501-8002","position":1,"is_corresponding":false},{"id":377606,"name":"Amanda Maher","orcid":null,"position":2,"is_corresponding":false},{"id":377607,"name":"Carol Persad","orcid":null,"position":3,"is_corresponding":false},{"id":1288733,"name":"Savannah G. Rose","orcid":"0000-0001-9784-6819","position":4,"is_corresponding":false},{"id":845141,"name":"Robert Koeppe","orcid":null,"position":5,"is_corresponding":false},{"id":325845,"name":"Bruno Giordani","orcid":"0000-0001-5921-3455","position":6,"is_corresponding":false},{"id":1421825,"name":"Sai Santosh Reddy Danda","orcid":null,"position":0,"is_corresponding":true}],"reference_count":41,"raw_metadata":null,"created_at":"2026-07-19T02:52:05.227140Z","pmid":"40799843","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}