{"doi":"10.1002/dad2.70187","title":"A lightweight machine learning tool for Alzheimer's disease prediction","abstract":"INTRODUCTION: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that needs better predictive tools. Using the National Alzheimer's Coordinating Center Uniform Data Set, this study developed machine learning (ML) models and a practical clinical tool for AD prediction. METHODS: Data from 52,537 individuals (22,371 with AD) and more than 200 variables were processed with MissForest imputation and genetic algorithm-based selection. Multiple ML models were trained, and interpretability was performed using SHAP and permutation importance. A LightGBM model was refined through iterative backward feature elimination (IBFE) followed by manual refinement. RESULTS: LightGBM performed best (receiver operating characteristic-area under the curve [ROC-AUC] 0.91, accuracy 82.0%). Key predictors included arthritis, age, body mass index, and heart rate. A 19-feature model retained accuracy (81.2%) and ROC-AUC (0.90). DISCUSSION: This lightweight tool predicts AD using mostly routine variables. Limitations include its cross-sectional nature, and would need external validation. An interactive web app and GitHub resource are available. Highlights: Developed a lightweight ML based tool using 19 routinely available features.The lightweight model achieved an ROC-AUC of 0.90 for Alzheimer's disease prediction on NACC multicenter data.Genetic algorithm, IBFE, and manual refinement enabled optimal feature selection.Tool hosted on an open-access platform for clinical and research use.SHAP analysis provided model interpretability and feature-level insights.","journal":"Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring","year":2025,"id":548588,"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":0.8522,"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":1441774,"name":"Tulika Nahar","orcid":"0000-0001-8766-1765","position":1,"is_corresponding":false},{"id":1442291,"name":"Arkansh Sharma","orcid":null,"position":2,"is_corresponding":false},{"id":1441775,"name":"Suhrud Panchawagh","orcid":"0000-0002-8606-4202","position":3,"is_corresponding":false},{"id":1441776,"name":"Omer Mohammed","orcid":"0000-0001-9166-6892","position":4,"is_corresponding":false},{"id":1441777,"name":"Muneeb Ahmad Muneer","orcid":"0009-0008-5653-1931","position":5,"is_corresponding":false},{"id":1441778,"name":"Devansh Mishra","orcid":"0000-0002-1584-3241","position":6,"is_corresponding":false},{"id":1019528,"name":"Amogh Verma","orcid":"0000-0003-2499-4874","position":7,"is_corresponding":false},{"id":1441779,"name":"Vivek Sanker","orcid":"0000-0003-0615-8397","position":8,"is_corresponding":false},{"id":1010668,"name":"Ayush Mishra","orcid":"0009-0008-1897-7748","position":9,"is_corresponding":false},{"id":1441780,"name":"Hardeep Singh Malhotra","orcid":"0000-0003-0438-4446","position":10,"is_corresponding":false},{"id":1441781,"name":"Ravindra Kumar Garg","orcid":"0000-0003-0044-7083","position":11,"is_corresponding":false},{"id":1441773,"name":"Vinay Suresh","orcid":"0000-0002-1401-9154","position":0,"is_corresponding":true}],"reference_count":26,"raw_metadata":null,"created_at":"2026-07-19T02:53:58.530220Z","pmid":"41256012","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":[]}