{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T21:58:50Z","timestamp":1785448730569,"version":"3.56.0"},"reference-count":35,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,4,28]],"date-time":"2024-04-28T00:00:00Z","timestamp":1714262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Mild Cognitive Impairment (MCI) is a cognitive state frequently observed in older adults, characterized by significant alterations in memory, thinking, and reasoning abilities that extend beyond typical cognitive decline. It is worth noting that around 10\u201315% of individuals with MCI are projected to develop Alzheimer\u2019s disease, effectively positioning MCI as an early stage of Alzheimer\u2019s. In this study, a novel approach is presented involving the utilization of eXtreme Gradient Boosting to predict the onset of Alzheimer\u2019s disease during the MCI stage. The methodology entails utilizing data from the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI). Through the analysis of longitudinal data, spanning from the baseline visit to the 12-month follow-up, a predictive model was constructed. The proposed model calculates, over a 36-month period, the likelihood of progression from MCI to Alzheimer\u2019s disease, achieving an accuracy rate of 85%. To further enhance the precision of the model, this study implements feature selection using the Recursive Feature Elimination technique. Additionally, the Shapley method is employed to provide insights into the model\u2019s decision-making process, thereby augmenting the transparency and interpretability of the predictions.<\/jats:p>","DOI":"10.3390\/info15050249","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T08:49:24Z","timestamp":1714380564000},"page":"249","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Predicting the Conversion from Mild Cognitive Impairment to Alzheimer\u2019s Disease Using an Explainable AI Approach"],"prefix":"10.3390","volume":"15","author":[{"given":"Gerasimos","family":"Grammenos","sequence":"first","affiliation":[{"name":"Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 7 Tsirigoti Square Corfu, 49100 Kerkira, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1892-0000","authenticated-orcid":false,"given":"Aristidis G.","family":"Vrahatis","sequence":"additional","affiliation":[{"name":"Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 7 Tsirigoti Square Corfu, 49100 Kerkira, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0053-7847","authenticated-orcid":false,"given":"Panagiotis","family":"Vlamos","sequence":"additional","affiliation":[{"name":"Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 7 Tsirigoti Square Corfu, 49100 Kerkira, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dean","family":"Palejev","sequence":"additional","affiliation":[{"name":"Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Themis","family":"Exarchos","sequence":"additional","affiliation":[{"name":"Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 7 Tsirigoti Square Corfu, 49100 Kerkira, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1136\/jnnp.2005.085332","article-title":"Mild cognitive impairment (MCI) in medical practice: A critical review of the concept and new diagnostic procedure. Report of the MCI Working Group of the European Consortium on Alzheimer\u2019s Disease","volume":"77","author":"Portet","year":"2006","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_2","unstructured":"(2023, May 06). Alzheimers Facts and Figures Report 2022. Available online: https:\/\/www.alz.org\/media\/Documents\/alzheimers-facts-and-figures-special-report-2022.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"(2023). 2023 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement., 19, 1598\u20131695.","DOI":"10.1002\/alz.13016"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101910","DOI":"10.1016\/j.compmedimag.2021.101910","article-title":"A predictive framework based on brain volume trajectories enabling early detection of Alzheimer\u2019s disease","volume":"90","author":"Mofrad","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"688926","DOI":"10.3389\/fnagi.2021.688926","article-title":"Predicting MCI to AD Conversation Using Integrated sMRI and rs-fMRI: Machine Learning and Graph Theory Approach","volume":"13","author":"Zhang","year":"2021","journal-title":"Front. Aging Neurosci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/S1474-4422(20)30440-3","article-title":"New Insights into Atypical Alzheimer\u2019s Disease in the Era of Biomarkers","volume":"20","author":"Yong","year":"2021","journal-title":"Lancet Neurol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1111\/joim.12816","article-title":"Biomarkers for Alzheimer\u2019s disease: Current status and prospects for the future","volume":"284","author":"Blennow","year":"2018","journal-title":"J. Intern. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102712","DOI":"10.1016\/j.nicl.2021.102712","article-title":"Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer\u2019s disease","volume":"31","author":"Bron","year":"2021","journal-title":"Neuroimage Clin."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vrahatis, A.G., Skolariki, K., Krokidis, M.G., Lazaros, K., Exarchos, T.P., and Vlamos, P. (2023). Revolutionizing the Early Detection of Alzheimer\u2019s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning. Sensors, 23.","DOI":"10.3390\/s23094184"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chang, C.H., Lin, C.H., and Lane, H.Y. (2021). Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer\u2019s Disease. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22052761"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"439698","DOI":"10.3389\/fnagi.2019.00095","article-title":"Cognitive profiling related to cerebral amyloid beta burden using machine learning approaches","volume":"11","author":"Ko","year":"2019","journal-title":"Front. Aging Neurosci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"144798","DOI":"10.3389\/fnins.2015.00307","article-title":"Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer\u2019s disease: A machine learning approach","volume":"9","author":"Salvatore","year":"2015","journal-title":"Front. Neurosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"513","DOI":"10.32604\/csse.2023.029608","article-title":"Prediction of Alzheimer\u2019s Using Random Forest with Radiomic Features","volume":"45","author":"Singh","year":"2022","journal-title":"Comput. Syst. Sci. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Akter, L. (2021, January 27\u201328). Dementia Identification for Diagnosing Alzheimer\u2019s Disease using XGBoost Algorithm. Proceedings of the 2021 International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2021, Dhaka, Bangladesh.","DOI":"10.1109\/ICICT4SD50815.2021.9396777"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"119541","DOI":"10.1016\/j.eswa.2023.119541","article-title":"A hybrid machine learning approach for prediction of conversion from mild cognitive impairment to dementia","volume":"217","author":"Bucholc","year":"2023","journal-title":"Expert. Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Balaji, P., Chaurasia, M.A., Bilfaqih, S.M., Muniasamy, A., and Alsid, L.E.G. (2023). Hybridized Deep Learning Approach for Detecting Alzheimer\u2019s Disease. Biomedicines, 11.","DOI":"10.3390\/biomedicines11010149"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6508","DOI":"10.1038\/s41598-022-10202-2","article-title":"In-depth insights into Alzheimer\u2019s disease by using explainable machine learning approach","volume":"12","author":"Bogdanovic","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_18","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_19","unstructured":"(2023, November 07). ADNI|About. Available online: https:\/\/adni.loni.usc.edu\/about\/."},{"key":"ref_20","unstructured":"(2023, November 07). ADNIMERGE: Clinical and Biomarker Data from All ADNI Protocols \u2022 ADNIMERGE. Available online: https:\/\/adni.bitbucket.io\/index.html."},{"key":"ref_21","unstructured":"(2023, November 07). ADNI_General Procedures Manual. Available online: https:\/\/adni.loni.usc.edu\/wp-content\/uploads\/2024\/02\/ADNI_General_Procedures_Manual_29Feb2024.pdf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1093\/bioinformatics\/17.6.520","article-title":"Missing value estimation methods for DNA microarrays","volume":"17","author":"Troyanskaya","year":"2001","journal-title":"Bioinformatics"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_24","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., and Gulin, A. (2019). CatBoost: Unbiased boosting with categorical features. arXiv."},{"key":"ref_25","unstructured":"Dorogush, A.V., Ershov, V., and Yandex, A.G. (2023, December 03). CatBoost: Gradient Boosting with Categorical Features Support. October 2018. Available online: https:\/\/arxiv.org\/abs\/1810.11363v1."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"12039","DOI":"10.1088\/1742-6596\/1955\/1\/012039","article-title":"Application of explainable machine learning based on Catboost in credit scoring","volume":"1955","author":"Zhenyu","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_27","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2023, December 03). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Available online: https:\/\/github.com\/Microsoft\/LightGBM."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene selection for cancer classification using support vector machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_29","first-page":"26","article-title":"Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization","volume":"17","author":"Wu","year":"2019","journal-title":"J. Electron. Sci. Technol."},{"key":"ref_30","unstructured":"Bloch, L., and Friedrich, C.M. (2021). Wireless Mobile Communication and Healthcare, Proceedings of the 9th EAI International Conference, MobiHealth 2020, Virtual Event,19 November 2020, Springer."},{"key":"ref_31","unstructured":"Lundberg, S.M., Allen, P.G., and Lee, S.-I. (2023, December 11). A Unified Approach to Interpreting Model Predictions. Available online: https:\/\/github.com\/slundberg\/shap."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"576029","DOI":"10.3389\/fneur.2020.576029","article-title":"Machine Learning for Diagnosis of AD and Prediction of MCI Progression from Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation","volume":"11","author":"Syaifullah","year":"2021","journal-title":"Front. Neurol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"509232","DOI":"10.3389\/fnagi.2020.00077","article-title":"Predicting Alzheimer\u2019s Disease Conversion from Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method with Multimodal Data","volume":"12","author":"Lin","year":"2020","journal-title":"Front. Aging Neurosci."},{"key":"ref_34","unstructured":"Anderson, N.H., and Woodburn, K. (2010). Companion Psychiatric Studies, Elsevier."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1001\/jamaneurol.2014.803","article-title":"The Preclinical Alzheimer Cognitive Composite: Measuring Amyloid-Related Decline","volume":"71","author":"Donohue","year":"2014","journal-title":"JAMA Neurol."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/5\/249\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:35:14Z","timestamp":1760106914000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/5\/249"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,28]]},"references-count":35,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["info15050249"],"URL":"https:\/\/doi.org\/10.3390\/info15050249","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,28]]}}}