{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T05:21:16Z","timestamp":1788326476811,"version":"build-2803163510"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"crossref","award":["ORF-2026-904"],"award-info":[{"award-number":["ORF-2026-904"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"crossref"}]},{"award":["ORF-2026-904"],"award-info":[{"award-number":["ORF-2026-904"]}],"id":[{"id":"https:\/\/ror.org\/02f81g417","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Alzheimer\u2019s disease and related dementias are projected to affect more than 150 million people worldwide by 2050. Early staging with validated instruments such as the Clinical Dementia Rating (CDR) scale is essential for timely intervention, yet access to clinician-administered CDR assessment remains constrained by workforce, time, and geographic barriers. This study complements a previously published machine learning pipeline for Alzheimer\u2019s disease prediction by addressing the downstream task of dementia staging. Because the global CDR score is already derived from the six sub-domain ratings through an established rule-based procedure, the contribution reported here lies not in discovering that mapping but in encoding it in a transparent, deployable form: an explainable decision tree classifier embedded in DiAbot, a large-language-model-fronted conversational system that supports self-administered CDR-style assessment. We extracted 13,453 CDR records from the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI), removed administrative variables, invalid entries, and missing rows (final n = 13,290), and trained decision tree classifiers under two impurity criteria, Information Gain and Gini Index, using a 70\/30 stratified record-level hold-out and ten-fold stratified record-level cross-validation. This classifier-level evaluation uses the six domain scores as recorded during ADNI\u2019s clinician-administered assessment, not scores elicited by the DiAbot chatbot; the trained classifier was separately embedded in a web application in which a prompt-engineered large language model conducts a CDR-style interview and normalizes responses to ordinal domain scores, but the end-to-end accuracy of that full conversational pipeline (chatbot elicitation through to final CDGLOBAL) has not yet been measured, and is not what the headline accuracy figures below report. The Information Gain Decision Tree reproduced the established mapping from the six CDR sub-domain scores to the CDGLOBAL with 99.86% accuracy under the record-level hold-out protocol (matching macro-averaged precision, recall, and F1-score), with a ten-fold record-level cross-validated mean of 99.81% (SD 0.07); this result represents fidelity to the established CDR scoring rule rather than independent dementia-diagnosis accuracy. Gini-based trees performed almost identically (99.79% hold-out, 99.74% cross-validated). Memory dominated feature importance, consistent with its role as the primary domain in the official CDR scoring algorithm. Residual misclassifications were confined to adjacent CDR stages. Because the CDGLOBAL is deterministically derived from the six sub-domain scores, these figures should be read throughout as evidence of high-fidelity reconstruction of the established CDR scoring relationship, not as general dementia-diagnosis accuracy comparable to imaging- or biomarker-based classifiers; further, participant-independent generalization remains unverified under the record-level protocol evaluated here. An interpretable classifier embedded in a conversational front-end can nonetheless make standardized CDR-style staging more widely accessible while preserving clinical inspectability; the resulting system is positioned as a screening-stage adjunct to, and not a replacement for, clinician-administered CDR assessment.<\/jats:p>","DOI":"10.3390\/bioengineering13091013","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T01:05:26Z","timestamp":1788224726000},"page":"1013","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DiAbot: A Conversational AI System Coupling Large Language Models with an Interpretable Decision Tree for CDR-Style Dementia Screening"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8427-5292","authenticated-orcid":false,"given":"Hala","family":"Alshamlan","sequence":"first","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alshamlan, H., Alwassel, A., Banafa, A., and Alsaleem, L. (2024). Improving Alzheimer\u2019s Disease Prediction with Different Machine Learning Approaches and Feature Selection Techniques. Diagnostics, 14.","DOI":"10.3390\/diagnostics14192237"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Balamurali, B.T., and Chen, J.-M. (2024). Performance Assessment of ChatGPT versus Bard in Detecting Alzheimer\u2019s Dementia. Diagnostics, 14.","DOI":"10.3390\/diagnostics14080817"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1177\/1533317511418954","article-title":"Validity, Reliability, and Feasibility of Clinical Staging Scales in Dementia: A Systematic Review","volume":"26","author":"Rikkert","year":"2011","journal-title":"Am. J. Alzheimer\u2019s Dis. Other Demen."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e081948","DOI":"10.1136\/bmj-2024-081948","article-title":"Age Against the Machine\u2014Susceptibility of Large Language Models to Cognitive Impairment: Cross Sectional Analysis","volume":"387","author":"Dayan","year":"2024","journal-title":"BMJ"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"953","DOI":"10.3233\/JAD-231426","article-title":"Simplifying Alzheimer\u2019s Disease Monitoring: A Novel Machine-Learning Approach to Estimate the Clinical Dementia Rating Sum of Box Changes Using the Mini-Mental State Examination and Functional Activities Questionnaire","volume":"99","author":"Sato","year":"2024","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.jpsychires.2008.04.014","article-title":"A Meta-Analysis of the Accuracy of the Mini-Mental State Examination in the Detection of Dementia and Mild Cognitive Impairment","volume":"43","author":"Mitchell","year":"2009","journal-title":"J. Psychiatr. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Miller, J.B., and Kauwe, J.S.K. (2020). Predicting Clinical Dementia Rating Using Blood RNA Levels. Genes, 11.","DOI":"10.3390\/genes11060706"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"AlShboul, R., Thabtah, F., Scott, A.J.W., and Wang, Y. (2023). The Application of Intelligent Data Models for Dementia Classification. Appl. Sci., 13.","DOI":"10.3390\/app13063612"},{"key":"ref_9","first-page":"3531","article-title":"Alzheimer\u2019s Disease Diagnosis Based on a Semantic Rule-Based Modeling and Reasoning Approach","volume":"69","author":"Shoaip","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_10","first-page":"370","article-title":"Decision Tree-Based Classification as a Support to Diagnosis in the Alzheimer\u2019s Disease Continuum Using Cerebrospinal Fluid Biomarkers: Insights from Automated Analysis","volume":"44","author":"Costa","year":"2022","journal-title":"Braz. J. Psychiatry"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.procs.2020.01.049","article-title":"Effective Diagnosis of Alzheimer\u2019s Disease Using Modified Decision Tree Classifier","volume":"165","author":"Naganandhini","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Morales-de-Jes\u00fas, V., G\u00f3mez-Adorno, H., Somodevilla-Garc\u00eda, M., and Vilari\u00f1o, D. (2021). Conversational System as Assistant Tool in Reminiscence Therapy for People with Early-Stage Alzheimer\u2019s. Healthcare, 9.","DOI":"10.3390\/healthcare9081036"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2200111","DOI":"10.1109\/JTEHM.2017.2752152","article-title":"Detecting Dementia Through Interactive Computer Avatars","volume":"5","author":"Tanaka","year":"2017","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tanaka, H., Adachi, H., Kazui, H., Ikeda, M., Kudo, T., and Nakamura, S. (2018). Detection of Dementia from Responses to Atypical Questions Asked by Embodied Conversational Agents. Proc. Interspeech, 1691\u20131695.","DOI":"10.21437\/Interspeech.2018-1514"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Routray, S., Samir, D., Pushya, G., and Srivastava, J. (2024, January 18\u201320). Early Detection of Dementia Using a Large Language Model-Powered Chatbot. Proceedings of the 2024 Eighth International Conference on Parallel, Distributed and Grid Computing (PDGC), Waknaghat, India.","DOI":"10.1109\/PDGC64653.2024.10984295"},{"key":"ref_16","unstructured":"(2026, May 05). Dementia Care Central. Available online: https:\/\/www.dementiacarecentral.com."},{"key":"ref_17","unstructured":"(2026, May 05). MDCalc\u2014Medical Calculators, Equations, Scores, and Guidelines. Available online: https:\/\/www.mdcalc.com."},{"key":"ref_18","unstructured":"(2026, May 05). National Alzheimer\u2019s Coordinating Center. Available online: https:\/\/naccdata.org."},{"key":"ref_19","unstructured":"(2026, May 05). UCSF Brain Health Registry. Available online: https:\/\/www.brainhealthregistry.org."},{"key":"ref_20","unstructured":"Alshamlan, H. (2026, August 24). DiAbot: Decision-Tree and Conversational-Interview Code Repository. Available online: https:\/\/github.com\/halshamlan\/Alzheimer-Predictor-."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1007\/978-3-642-13769-3_55","article-title":"Effective Diagnosis of Alzheimer\u2019s Disease by Means of Association Rules","volume":"Volume 6076","author":"Chaves","year":"2010","journal-title":"Hybrid Artificial Intelligence Systems"},{"key":"ref_22","unstructured":"Hashmi, A. (2026). Early Detection of Alzheimer\u2019s Disease Using Explainable Machine Learning on Clinical Biomarkers: A Multi-Class Classification Study Using the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) Dataset. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yi, F., Yang, H., Chen, D., Qin, Y., Han, H., Cui, J., Bai, W., Ma, Y., Zhang, R., and Yu, H. (2023). XGBoost-SHAP-based interpretable diagnostic framework for Alzheimer\u2019s disease. BMC Med. Inform. Decis. Mak., 23.","DOI":"10.1186\/s12911-023-02238-9"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xu, J., and Costen, F. (2026). Machine Learning-Based Multiclass Classification of Cognitive Stages Using Plasma Biomarkers, Clinical Assessments, and Genetic Features: A Repeated, Nested Cross-Validation Study in ADNI with External Evaluation in CNTN. Diagnostics, 16.","DOI":"10.20944\/preprints202605.0498.v1"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1001\/archneur.64.5.725","article-title":"Patient\u2019s Rating of Cognitive Ability: Using the AD8, a Brief Informant Interview, as a Self-Rating Tool to Detect Dementia","volume":"64","author":"Galvin","year":"2007","journal-title":"Arch. Neurol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Miranda, J.J., L\u00facar-Flores, M., Bernabe-Ortiz, A., Calvo, R.A., Cardenas, M.K., Custodio, N., Cutip\u00e9-C\u00e1rdenas, Y.L., Diez-Canseco, F., Hawkins, J., and Landeiro, F. (2025). Rethinking and Transforming Health Systems for Dementia Care in Low- and Middle-Income Country Settings. PLoS Glob. Public Health, 5.","DOI":"10.1371\/journal.pgph.0005419"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/9\/1013\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T04:43:02Z","timestamp":1788324182000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/9\/1013"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":26,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["bioengineering13091013"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13091013","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]}}}