{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:10:06Z","timestamp":1791915006730,"version":"4.3.4"},"reference-count":17,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Ministry of Education (MOE) and the Gangwon State (G.S.), Republic of Korea","award":["2025-RISE-10-001"],"award-info":[{"award-number":["2025-RISE-10-001"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Life"],"abstract":"<jats:p>Early and accurate diagnosis of dementia is essential to improving patient outcomes and reducing societal burden. The Mini-Mental State Examination (MMSE) is widely used to assess cognitive function, yet traditional statistical and machine learning approaches often face limitations in capturing nonlinear interactions and subtle decline patterns. This study developed a novel deep learning-based dementia prediction model using MMSE data collected from domestic clinical settings and compared its performance with traditional machine learning models. A notable strength of this work lies in its use of item-level MMSE features combined with explainable AI (SHAP analysis), enabling both high predictive accuracy and clinical interpretability\u2014an advancement over prior approaches that primarily relied on total scores or linear modeling. Data from 164 participants, classified into cognitively normal, mild cognitive impairment (MCI), and dementia groups, were analyzed. Individual MMSE items and total scores were used as input features, and the dataset was divided into training and validation sets (8:2 split). A fully connected neural network with regularization techniques was constructed and evaluated alongside Random Forest and support vector machine (SVM) classifiers. Model performance was assessed using accuracy, F1-score, confusion matrices, and receiver operating characteristic (ROC) curves. The deep learning model achieved the highest performance (accuracy 0.90, F1-score 0.90), surpassing Random Forest (0.86) and SVM (0.82). SHAP analysis identified Q11 (immediate memory), Q12 (calculation), and Q17 (drawing shapes) as the most influential variables, aligning with clinical diagnostic practices. These findings suggest that deep learning not only enhances predictive accuracy but also offers interpretable insights aligned with clinical reasoning, underscoring its potential utility as a reliable tool for early dementia diagnosis. However, the study is limited by the use of data from a single clinical site with a relatively small sample size, which may restrict generalizability. Future research should validate the model using larger, multi-institutional, and multimodal datasets to strengthen clinical applicability and robustness.<\/jats:p>","DOI":"10.3390\/life15101544","type":"journal-article","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T14:27:42Z","timestamp":1759328862000},"page":"1544","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MMSE-Based Dementia Prediction: Deep vs. Traditional Models"],"prefix":"10.3390","volume":"15","author":[{"given":"Yuyeon","family":"Jung","sequence":"first","affiliation":[{"name":"Department of Dental Hygiene, College of Medical Science, Konyang University, Daejeon 35365, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeji","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Medical Engineering, College of Medical Convergence, Catholic Kwandong University, Gangneung-si 25601, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaehyun","family":"Jo","sequence":"additional","affiliation":[{"name":"Department of Medical Engineering, College of Medical Convergence, Catholic Kwandong University, Gangneung-si 25601, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7273-7279","authenticated-orcid":false,"given":"Jinhyoung","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Biomedical Management, College of Medical Convergence, Catholic Kwandong University, Gangneung-si 25601, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2023). Dementia, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/0022-3956(75)90026-6","article-title":"\u201cMini-mental state\u201d: A practical method for grading the cognitive state of patients for the clinician","volume":"12","author":"Folstein","year":"1975","journal-title":"J. Psychiatr. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e70025","DOI":"10.1002\/hsr2.70025","article-title":"Deep learning techniques for Alzheimer\u2019s disease detection in 3D imaging: A systematic review","volume":"7","author":"Ali","year":"2024","journal-title":"Health Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chun, M.Y., Park, C.J., Kim, J., Jeong, J.H., Jang, H., Kim, K., and Seo, S.W. (2022). Prediction of conversion to dementia using interpretable machine learning in patients with amnestic mild cognitive impairment. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.898940"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Namgung, E., Kim, Y.S., Kwon, S.U., and Kang, D.W. (2025). Predicting cognitive decline in cognitively impaired patients with ischemic stroke with high risk of cerebral hemorrhage: A machine learning approach. Front. Neurol., 16.","DOI":"10.3389\/fneur.2025.1569073"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s40708-024-00222-1","article-title":"Interpreting artificial intelligence models: A systematic review on the application of LIME and SHAP in Alzheimer\u2019s disease detection","volume":"11","author":"Vimbi","year":"2024","journal-title":"Brain Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"70676","DOI":"10.1109\/ACCESS.2024.3402543","article-title":"Keeping deep learning models in check: A history-based approach to mitigate overfitting","volume":"12","author":"Li","year":"2024","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Richter-Laskowska, M., Sobotnicka, E., and Bednorz, A. (2025). Cognitive performance classification of older patients using machine learning and electronic medical records. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-90460-y"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"15333175241308645","DOI":"10.1177\/15333175241308645","article-title":"A Study on Machine Learning Models in Detecting Cognitive Impairments in Alzheimer\u2019s Patients Using Cerebrospinal Fluid Biomarkers","volume":"39","author":"Tiwari","year":"2024","journal-title":"Am. J. Alzheimers Dis. Other Demen."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106093","DOI":"10.1016\/j.ijmedinf.2025.106093","article-title":"Explainable machine learning models for early Alzheimer\u2019s disease detection using multimodal clinical data","volume":"204","author":"Soladoye","year":"2025","journal-title":"Int. J. Med. Inform."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Linardatos, P., Papastefanopoulos, V., and Kotsiantis, S. (2021). Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy, 23.","DOI":"10.3390\/e23010018"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s40520-025-03112-6","article-title":"Clinical prediction models using artificial intelligence approaches in dementia","volume":"37","author":"Veronese","year":"2025","journal-title":"Aging Clin. Exp. Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hidayaturrohman, Q.A., and Hanada, E. (2025). A Comparative Analysis of Hyper-Parameter Optimization Methods for Predicting Heart Failure Outcomes. Appl. Sci., 15.","DOI":"10.3390\/app15063393"},{"key":"ref_16","unstructured":"(2024, September 24). Available online: https:\/\/www.nature.com\/collections\/fifhafhibf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s10916-023-01906-7","article-title":"Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions","volume":"47","author":"Javeed","year":"2023","journal-title":"J. Med. Syst."}],"container-title":["Life"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1729\/15\/10\/1544\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T14:32:26Z","timestamp":1759329146000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1729\/15\/10\/1544"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,1]]},"references-count":17,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["life15101544"],"URL":"https:\/\/doi.org\/10.3390\/life15101544","relation":{},"ISSN":["2075-1729"],"issn-type":[{"value":"2075-1729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,1]]}}}