{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T09:27:05Z","timestamp":1786613225115,"version":"build-2736575974"},"reference-count":42,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Frailty syndrome and Alzheimer\u2019s disease are prevalent conditions in the elderly that are associated with aging, decreased quality of life, and a significant healthcare burden. Evidence for a relationship between physical frailty and neurodegenerative decline is accumulating. This study analyzed two independent datasets, a frailty dataset based on gait and mobility parameters and an AD dataset with clinical, functional and lifestyle variables, in order to evaluate and compare their classification performance using machine learning. Features were optimized using dimensionality reduction techniques to keep predictors of clinical significance and hyperparameter optimized Random Forest models were built to develop the best model. Evaluation was performed with Accuracy, F1-score, Matthews Correlation Coefficient and Area Under the Curve. The results showed that the models constructed on the whole AD dataset achieved maximum predictive power with an accuracy of 0.946, which was slightly increased to an accuracy of 0.948 after the selection of significant features. Diagnostic models based on frailty were able to demonstrate an ACC predictive capacity of 0.6418, and in terms of feature selection, improvements appeared in all indicators. Regarding the features derived from Alzheimer\u2019s disease associated with geriatric frailty, they managed to surpass the ACC frailty features of 0.741 alone, suggesting some intercalation mechanisms between neurodegeneration and physical vulnerability. These findings show that machine learning algorithms accompanied by feature selection improve clinical discrimination and prediction of frailty and neurodegenerative disorders, which offers a promising aspect for geriatric assessment. The frailty models analyzed demonstrated an ACC predictive capacity of 0.6418, even though feature selection improved all indicators. Alzheimer\u2019s disease-derived features associated with frailty outperformed features in the frailty dataset with an ACC of 0.741, suggesting the mechanism of overlap between neurodegeneration and physical vulnerability. These results support the theory of a motor-cognitive aging continuum, indicating that algorithmic machine learning techniques coupled with feature selection mainly provide computational validation for the biological intersection of neurodegeneration and physical frailty, rather than forming an independent predictive clinical model. Using these algorithms the study highlights shared pathophysiological mechanisms, providing a significant insight into systemic geriatric deterioration.<\/jats:p>","DOI":"10.3390\/life16081324","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T08:06:16Z","timestamp":1786608376000},"page":"1324","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative Analysis by Machine Learning of Geriatric Frailty and Alzheimer\u2019s Disease Classification Using Independent Datasets"],"prefix":"10.3390","volume":"16","author":[{"given":"L\u0103cr\u0103mioara Lumini\u021ba","family":"Apescaritei Apostol","sequence":"first","affiliation":[{"name":"Research Centre in the Medical-Pharmaceutical Field, Medicine and Pharmacy Faculty, \u201cDun\u0103rea de Jos\u201d University of Galati, 800010 Gala\u021bi, Romania"},{"name":"\u201cSf. Apostol Andrei\u201d County Emergency Clinical Hospital, 800578 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudia Simona","family":"\u0218tefan","sequence":"additional","affiliation":[{"name":"Research Centre in the Medical-Pharmaceutical Field, Medicine and Pharmacy Faculty, \u201cDun\u0103rea de Jos\u201d University of Galati, 800010 Gala\u021bi, Romania"},{"name":"\u201cSf. Apostol Andrei\u201d County Emergency Clinical Hospital, 800578 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7703-7506","authenticated-orcid":false,"given":"Mihai","family":"Grecu","sequence":"additional","affiliation":[{"name":"Research Centre in the Medical-Pharmaceutical Field, Medicine and Pharmacy Faculty, \u201cDun\u0103rea de Jos\u201d University of Galati, 800010 Gala\u021bi, Romania"},{"name":"\u201cSf. Apostol Andrei\u201d County Emergency Clinical Hospital, 800578 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5934-329X","authenticated-orcid":false,"given":"Simona","family":"Moldovanu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, \u201cDunarea de Jos\u201d University of Galati, 800146 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5438-666X","authenticated-orcid":false,"given":"Gabriela Isabela","family":"Verga","sequence":"additional","affiliation":[{"name":"Research Centre in the Medical-Pharmaceutical Field, Medicine and Pharmacy Faculty, \u201cDun\u0103rea de Jos\u201d University of Galati, 800010 Gala\u021bi, Romania"},{"name":"\u201cSf. Ioan\u201d Emergency Clinical Hospital for Children, 800487 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mihaela","family":"Lungu","sequence":"additional","affiliation":[{"name":"Research Centre in the Medical-Pharmaceutical Field, Medicine and Pharmacy Faculty, \u201cDun\u0103rea de Jos\u201d University of Galati, 800010 Gala\u021bi, Romania"},{"name":"\u201cSf. Apostol Andrei\u201d County Emergency Clinical Hospital, 800578 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4762-9581","authenticated-orcid":false,"given":"Gabriel Ioan","family":"Prada","sequence":"additional","affiliation":[{"name":"Clinic Department, \u201cCarol Davila\u201d University of Medicine and Pharmacy, 050711 Bucharest, Romania"},{"name":"National Institute of Gerontology and Geriatrics \u201cAna Aslan\u201d, 011241 Bucharest, Romania"},{"name":"Academy of Romanian Scientists, 050094 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aurelia","family":"Romila","sequence":"additional","affiliation":[{"name":"Research Centre in the Medical-Pharmaceutical Field, Medicine and Pharmacy Faculty, \u201cDun\u0103rea de Jos\u201d University of Galati, 800010 Gala\u021bi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"993","DOI":"10.3233\/JAD-170963","article-title":"Different models of cognitive frailty and health outcomes and cognitive aspects in old age: From epidemiology to prevention","volume":"62","author":"Panza","year":"2018","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"xi","DOI":"10.1016\/j.cger.2017.05.001","article-title":"The new geriatric giants","volume":"33","author":"Morley","year":"2017","journal-title":"Clin. Geriatr. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/S0025-7125(02)00010-X","article-title":"Dementia epidemiology","volume":"86","author":"Kukull","year":"2002","journal-title":"Med. Clin. N. Am."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"S3","DOI":"10.1097\/00019442-199821001-00002","article-title":"Epidemiology of Dementia and Alzheimer\u2019s Disease","volume":"6","author":"Hendrie","year":"1998","journal-title":"Am. J. Geriatr. Psychiatry"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1056\/NEJMra2301292","article-title":"Frailty in Older Adults","volume":"391","author":"Kim","year":"2024","journal-title":"N. Engl. J. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1093\/ageing\/afaa219","article-title":"Prevalence of frailty in 62 countries worldwide: A systematic review and meta-analysis of population-based studies","volume":"50","author":"Sezgin","year":"2021","journal-title":"Age Ageing"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bai, G., Wang, Y., Kuja-Halkola, R., Li, X., Tomata, Y., Karlsson, I.K., Pedersen, N.L., H\u00e4gg, S., and Jylh\u00e4v\u00e4, J. (2021). Frailty and the risk of dementia: Is the association explained by shared environmental and genetic factors?. BMC Med., 19.","DOI":"10.1186\/s12916-021-02104-3"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1017\/S1041610220004160","article-title":"Frailty and dementia: What can the body tell us about the brain?","volume":"33","author":"Liu","year":"2021","journal-title":"Int. Psychogeriatr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1136\/jnnp-2021-327396","article-title":"Frailty, lifestyle, genetics and dementia risk","volume":"93","author":"Ward","year":"2022","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lim, W.-S., Canevelli, M., and Cesari, M. (2018). Editorial: Dementia, Frailty and Aging. Front. Med., 5.","DOI":"10.3389\/fmed.2018.00168"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"146","DOI":"10.35219\/efms.2025.2.10","article-title":"Impact of exercise on geriatric neurodegenerative disease management","volume":"2","author":"Grecu","year":"2026","journal-title":"Ann. Dunarea Jos Univ. Galati Fascicle XV Phys. Educ. Sport Manag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1016\/S0140-6736(15)00516-4","article-title":"The World report on ageing and health: A policy framework for healthy ageing","volume":"387","author":"Beard","year":"2016","journal-title":"Lancet"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1016\/S0140-6736(17)31363-6","article-title":"Dementia prevention, intervention, and care","volume":"390","author":"Livingston","year":"2017","journal-title":"Lancet"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.12680\/balneo.2026.1024","article-title":"Early-stage research and new pharmacological targets in the management of Alzheimer\u2019s dementia\u2014A review of the literature","volume":"17","author":"Grecu","year":"2026","journal-title":"Balneo PRM Res. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e70312","DOI":"10.1002\/hsr2.70312","article-title":"The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency","volume":"8","author":"Faiyazuddin","year":"2025","journal-title":"Health Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e188","DOI":"10.7861\/fhj.2021-0095","article-title":"Artificial Intelligence in Healthcare: Transforming Medical Practice","volume":"8","author":"Bajwa","year":"2021","journal-title":"Future Healthc. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.alit.2025.06.005","article-title":"Artificial intelligence in clinical data analysis: A review of large language models, foundation models, digital twins, and allergy applications","volume":"74","author":"Fuse","year":"2025","journal-title":"Allergol. Int."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Al-Nafjan, A., Aljuhani, A., Alshebel, A., Alharbi, A., and Alshehri, A. (2025). Artificial Intelligence in Predictive Healthcare: A Systematic Review. J. Clin. Med., 14.","DOI":"10.3390\/jcm14196752"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"69","DOI":"10.58496\/BJML\/2024\/007","article-title":"Random Forest Algorithm Overview","volume":"2024","author":"Salman","year":"2024","journal-title":"Babylon. J. Mach. Learn."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jneumeth.2017.12.010","article-title":"Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healthy elderly, MCI, cMCI and alzheimer\u2019s disease patients: From the alzheimer\u2019s disease neuroimaging initiative (ADNI) database","volume":"302","author":"Dimitriadis","year":"2018","journal-title":"J. Neurosci. Methods"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"962","DOI":"10.4103\/1673-5374.233433","article-title":"How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer\u2019s disease: From Alzheimer\u2019s disease neuroimaging initiative (ADNI) database","volume":"13","author":"Dimitriadis","year":"2018","journal-title":"Neural Regen. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.nicl.2014.08.023","article-title":"Random Forest ensembles for detection and prediction of Alzheimer\u2019s disease with a good between-cohort robustness","volume":"6","author":"Lebedev","year":"2014","journal-title":"NeuroImage Clin."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Song, M., Jung, H., Lee, S., Kim, D., and Ahn, M. (2021). Diagnostic Classification and Biomarker Identification of Alzheimer\u2019s Disease with Random Forest Algorithm. Brain Sci., 11.","DOI":"10.3390\/brainsci11040453"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Velazquez, M., and Lee, Y. (2021). Random forest model for feature-based Alzheimer\u2019s disease conversion prediction from early mild cognitive impairment subjects. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0244773"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Moore, P.J., Lyons, T.J., and Gallacher, J. (2019). Random forest prediction of Alzheimer\u2019s disease using pairwise selection from time series data. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0211558"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Song, Y., and Liu, Y. (2025). Machine learning algorithms to predict frailty in older adults in China: A cross-sectional study. BMC Geriatr., 25.","DOI":"10.1186\/s12877-025-06738-3"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e16678","DOI":"10.2196\/16678","article-title":"Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches","volume":"8","author":"Tarekegn","year":"2020","journal-title":"JMIR Med. Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"231","DOI":"10.4258\/hir.2022.28.3.231","article-title":"Development of a Frailty Detection Model Using Machine Learning with the Korean Frailty and Aging Cohort Study Data","volume":"28","author":"Koo","year":"2022","journal-title":"Healthc. Inform. Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wu, Y., Jia, M., Xiang, C., and Fang, Y. (2022). Latent trajectories of frailty and risk prediction models among geriatric community dwellers: An interpretable machine learning perspective. BMC Geriatr., 22.","DOI":"10.1186\/s12877-022-03576-5"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lian, L., Li, Y., Han, S., Meng, R., Wang, S., and Wang, M. (2025). Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services. arXiv.","DOI":"10.1145\/3772726.3772876"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Barua, K., Mahmud, T., Barua, A., Sharmen, N., Basnin, N., Islam, D., Hossain, M.S., Andersson, K., and Hossain, S. (2023, January 13\u201315). Explainable AI-Based Humerus Fracture Detection and Classification from X-Ray Images. Proceedings of the 26th International Conference on Computer and Information Technology (ICCIT), Cox\u2019s Bazar, Bangladesh.","DOI":"10.1109\/ICCIT60459.2023.10441124"},{"key":"ref_32","first-page":"60","article-title":"Artificial Intelligence (AI) Tools Constructed via the 5-Steps Rule for Predicting Post-Translational Modifications","volume":"3","author":"Chou","year":"2019","journal-title":"Trends Artif. Intell."},{"key":"ref_33","unstructured":"Singh, A. (2026, April 20). ROC Curve Unveiled: Decoding Model Performance Metrics. Medium. Available online: https:\/\/medium.com\/@abhaysingh71711\/roc-curve-unveiled-decoding-model-performance-metrics-59eb8a5e2556."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Feng, K., Hong, H., Tang, K., and Wang, J. (2019). Decision Making with Machine Learning and ROC Curves. arXiv.","DOI":"10.2139\/ssrn.3382962"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"65","DOI":"10.70594\/brain\/v10.i2\/7","article-title":"ROC Curve Analysis for Classification of Road Defects","volume":"10","author":"Nguyen","year":"2019","journal-title":"Broad Res. Artif. Intell. Neurosci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1038\/s41746-020-00324-0","article-title":"The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database","volume":"3","author":"Benjamens","year":"2020","journal-title":"npj Digit. Med."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e1356","DOI":"10.1002\/widm.1356","article-title":"Bias in data-driven artificial intelligence systems\u2014An introductory survey","volume":"10","author":"Ntoutsi","year":"2020","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1145\/3561048","article-title":"Explainable AI (XAI): Core Ideas, Techniques, and Solutions","volume":"55","author":"Dwivedi","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1038\/s41597-026-06854-8","article-title":"Database for Prevalence and Determinants of Frailty in the Elderly with Quantifying Functional Mobility","volume":"13","author":"Amjad","year":"2026","journal-title":"Sci. Data"},{"key":"ref_40","unstructured":"El Kharoua, R. (2026, April 20). Alzheimer\u2019s Disease Dataset. Kaggle. Available online: https:\/\/www.kaggle.com\/dsv\/8668279."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The use of the area under the roc curve in the evaluation of machine learning algorithms","volume":"30","author":"Bradley","year":"1997","journal-title":"Pattern Recogn."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1212\/01.wnl.0000324864.81179.6a","article-title":"Physical frailty in older persons is associated with Alzheimer disease pathology","volume":"71","author":"Buchman","year":"2008","journal-title":"Neurology"}],"container-title":["Life"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1729\/16\/8\/1324\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T08:31:59Z","timestamp":1786609919000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1729\/16\/8\/1324"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,13]]},"references-count":42,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["life16081324"],"URL":"https:\/\/doi.org\/10.3390\/life16081324","relation":{},"ISSN":["2075-1729"],"issn-type":[{"value":"2075-1729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,13]]}}}