{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T05:27:04Z","timestamp":1786685224555,"version":"build-2736575974"},"reference-count":33,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union\u2014NextGenerationEU","doi-asserted-by":"publisher","award":["CN000013"],"award-info":[{"award-number":["CN000013"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Italian Ministry of Universities and Research","award":["D93C22000430001"],"award-info":[{"award-number":["D93C22000430001"]}]},{"award":["D93C22000430001"],"award-info":[{"award-number":["D93C22000430001"]}],"id":[{"id":"https:\/\/ror.org\/0341vw408","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014041","name":"Alzheimer\u2019s Disease Neuroimaging Initiative","doi-asserted-by":"crossref","award":["U01 AG024904"],"award-info":[{"award-number":["U01 AG024904"]}],"id":[{"id":"10.13039\/100014041","id-type":"DOI","asserted-by":"crossref"}]},{"award":["U01 AG024904"],"award-info":[{"award-number":["U01 AG024904"]}],"id":[{"id":"https:\/\/ror.org\/01j20wc74","id-type":"ROR","asserted-by":"publisher"}]},{"name":"DOD ADNI","award":["W81XWH-12-2-0012"],"award-info":[{"award-number":["W81XWH-12-2-0012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Feature selection (FS) plays a critical role in sensor-based predictive modeling for Alzheimer\u2019s disease (AD), where heterogeneous clinical and neuroimaging measurements generate high-dimensional data with varying degrees of missingness due to incomplete clinical assessment of patients. Effective dimensionality reduction is essential to improve model interpretability, robustness, and generalization performance in sensor-driven healthcare applications. However, a systematic analysis of the interplay between FS strategies, missing-data handling, and prognostic modeling in sensor-derived AD data remains underexplored. In this study, we present a comprehensive and methodologically rigorous evaluation framework for AD prediction using multimodal data from the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) cohort. We jointly investigate multiple FS techniques and prognostic models on imputed datasets, systematically varying the number of top-ranked features. To ensure robustness, a K-fold cross-validation (CV) procedure is adopted and only features consistently selected across folds (intersection-based stability criterion) are retained. These stable feature subsets are subsequently evaluated on a test set. To further assess robustness to incomplete sensor measurements, we conduct a sensitivity analysis by varying the tolerated missingness thresholds for feature inclusion, reflecting realistic scenarios of incomplete clinical data availability. In this phase, XGBoost is employed both as a prognostic model and as an embedded FS method, exploiting its native capability to handle missing values and to provide feature-importance rankings based on predictive contribution. The proposed framework enables a systematic assessment of FS stability, predictive performance, and resilience to missing sensor data. Results provide practical methodological guidelines for the development of reliable and generalizable sensor-driven prognostic models for AD in real-world clinical environments.<\/jats:p>","DOI":"10.3390\/s26165109","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T12:53:48Z","timestamp":1786539228000},"page":"5109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feature Selection Analysis and Robustness to Missing Data for Sensor-Based Prognostic Modeling of Alzheimer\u2019s Disease Progression"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0774-7337","authenticated-orcid":false,"given":"Vito Ivano","family":"D\u2019Alessandro","sequence":"first","affiliation":[{"name":"Department of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2667-8982","authenticated-orcid":false,"given":"Filippo","family":"Attivissimo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiziana","family":"Basileo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0057-7312","authenticated-orcid":false,"given":"Luisa","family":"De Palma","sequence":"additional","affiliation":[{"name":"Department of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna Maria Lucia","family":"Lanzolla","sequence":"additional","affiliation":[{"name":"Department of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4166-7755","authenticated-orcid":false,"given":"Attilio","family":"Di Nisio","sequence":"additional","affiliation":[{"name":"Department of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"The Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"ref_1","unstructured":"Alzheimer\u2019s Association (2025). 2025 Alzheimer\u2019s Disease Facts and Figures. Alzheimer\u2019s Dement., 21, e70235."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A Review of Feature Selection Techniques in Bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Attivissimo, F., De Palma, L., Di Nisio, A., Scarpetta, M., and Lanzolla, A.M.L. (2023). 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