{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T17:56:08Z","timestamp":1780595768632,"version":"3.54.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100020771","name":"Natural Science Foundation for Young Scientists of Shanxi Province","doi-asserted-by":"publisher","award":["201901D211330"],"award-info":[{"award-number":["201901D211330"]}],"id":[{"id":"10.13039\/501100020771","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020771","name":"Natural Science Foundation for Young Scientists of Shanxi Province","doi-asserted-by":"publisher","award":["202103021223242"],"award-info":[{"award-number":["202103021223242"]}],"id":[{"id":"10.13039\/501100020771","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["81973154"],"award-info":[{"award-number":["81973154"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Due to the class imbalance issue faced when Alzheimer\u2019s disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework, extreme gradient boosting-Shapley additive explanations (XGBoost-SHAP), to handle the imbalance among different AD progression statuses at the algorithmic level. We also sought to achieve multiclassification of NC, MCI, and AD.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We obtained patient data from the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) database, including clinical information, neuropsychological test results, neuroimaging-derived biomarkers, and APOE-\u03b54 gene statuses. First, three feature selection algorithms were applied, and they were then included in the XGBoost algorithm. Due to the imbalance among the three classes, we changed the sample weight distribution to achieve multiclassification of NC, MCI, and AD. Then, the SHAP method was linked to XGBoost to form an interpretable framework. This framework utilized attribution ideas that quantified the impacts of model predictions into numerical values and analysed them based on their directions and sizes. Subsequently, the top 10 features (optimal subset) were used to simplify the clinical decision-making process, and their performance was compared with that of a random forest (RF), Bagging, AdaBoost, and a naive Bayes (NB) classifier. Finally, the National Alzheimer\u2019s Coordinating Center (NACC) dataset was employed to assess the impact path consistency of the features within the optimal subset.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Compared to the RF, Bagging, AdaBoost, NB and XGBoost (unweighted), the interpretable framework had higher classification performance with accuracy improvements of 0.74%, 0.74%, 1.46%, 13.18%, and 0.83%, respectively. The framework achieved high sensitivity (81.21%\/74.85%), specificity (92.18%\/89.86%), accuracy (87.57%\/80.52%), area under the receiver operating characteristic curve (AUC) (0.91\/0.88), positive clinical utility index (0.71\/0.56), and negative clinical utility index (0.75\/0.68) on the ADNI and NACC datasets, respectively. In the ADNI dataset, the top 10 features were found to have varying associations with the risk of AD onset based on their SHAP values. Specifically, the higher SHAP values of <jats:italic>CDRSB<\/jats:italic>, <jats:italic>ADAS13<\/jats:italic>, <jats:italic>ADAS11<\/jats:italic>, <jats:italic>ventricle volume<\/jats:italic>, <jats:italic>ADASQ4<\/jats:italic>, and <jats:italic>FAQ<\/jats:italic> were associated with higher risks of AD onset. Conversely, the higher SHAP values of <jats:italic>LDELTOTAL<\/jats:italic>, <jats:italic>mPACCdigit<\/jats:italic>, <jats:italic>RAVLT_immediate<\/jats:italic>, and <jats:italic>MMSE<\/jats:italic> were associated with lower risks of AD onset. Similar results were found for the NACC dataset.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The proposed interpretable framework contributes to achieving excellent performance in imbalanced AD multiclassification tasks and provides scientific guidance (optimal subset) for clinical decision-making, thereby facilitating disease management and offering new research ideas for optimizing AD prevention and treatment programs.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-023-02238-9","type":"journal-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T13:02:28Z","timestamp":1690290148000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":144,"title":["XGBoost-SHAP-based interpretable diagnostic framework for alzheimer\u2019s disease"],"prefix":"10.1186","volume":"23","author":[{"given":"Fuliang","family":"Yi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Durong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjuan","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenlin","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongmei","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"issue":"1","key":"2238_CR1","doi-asserted-by":"publisher","first-page":"355","DOI":"10.3233\/JAD-201377","volume":"81","author":"MJ Kleiman","year":"2021","unstructured":"Kleiman MJ, Barenholtz E, Galvin JE, Initiative AsDN. Screening for early-stage Alzheimer\u2019s disease using optimized feature sets and machine learning. J Alzheimers Dis. 2021;81(1):355\u201366.","journal-title":"J Alzheimers Dis"},{"issue":"3","key":"2238_CR2","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.3233\/JAD-201438","volume":"80","author":"S Nagaraj","year":"2021","unstructured":"Nagaraj S, Duong TQ. Deep learning and risk score classification of mild cognitive impairment and Alzheimer\u2019s Disease. J Alzheimers Dis. 2021;80(3):1079\u201390.","journal-title":"J Alzheimers Dis"},{"issue":"2","key":"2238_CR3","doi-asserted-by":"publisher","first-page":"545","DOI":"10.3233\/JAD-191163","volume":"74","author":"H Patel","year":"2020","unstructured":"Patel H, Iniesta R, Stahl D, Dobson RJ, Newhouse SJ. Working towards a blood-derived gene expression Biomarker Specific for Alzheimer\u2019s Disease. J Alzheimers Dis. 2020;74(2):545\u201361.","journal-title":"J Alzheimers Dis"},{"issue":"7715","key":"2238_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/d41586-018-05717-6","volume":"559","author":"R Hodson","year":"2018","unstructured":"Hodson R. Alzheimer\u2019s disease. Nature. 2018;559(7715):1.","journal-title":"Nature"},{"issue":"1","key":"2238_CR5","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1017\/S1092852918001347","volume":"24","author":"ND Anderson","year":"2019","unstructured":"Anderson ND. State of the science on mild cognitive impairment (MCI). CNS Spectr. 2019;24(1):78\u201387.","journal-title":"CNS Spectr"},{"key":"2238_CR6","unstructured":"Gauthier S, Rosa-Neto P, Morais J, Webster C. World Alzheimer Report 2021-Journey through the diagnosis of Dementia.(2021). London, England: Alzheimer\u2019s Disease International, 314."},{"issue":"6","key":"2238_CR7","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1093\/brain\/awaa137","volume":"143","author":"S Qiu","year":"2020","unstructured":"Qiu S, Joshi PS, Miller MI, Xue C, Zhou X, Karjadi C, Chang GH, Joshi AS, Dwyer B, Zhu S. Development and validation of an interpretable deep learning framework for Alzheimer\u2019s disease classification. Brain. 2020;143(6):1920\u201333.","journal-title":"Brain"},{"key":"2238_CR8","doi-asserted-by":"publisher","first-page":"1850909","DOI":"10.1155\/2017\/1850909","volume":"2017","author":"P Battista","year":"2017","unstructured":"Battista P, Salvatore C, Castiglioni I. Optimizing neuropsychological assessments for cognitive, behavioral, and functional impairment classification: a machine learning study. Behav Neurol. 2017;2017:1850909.","journal-title":"Behav Neurol"},{"issue":"6","key":"2238_CR9","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1007\/s00415-018-9016-3","volume":"266","author":"A Chandra","year":"2019","unstructured":"Chandra A, Dervenoulas G, Politis M. Magnetic resonance imaging in Alzheimer\u2019s disease and mild cognitive impairment. J Neurol. 2019;266(6):1293\u2013302.","journal-title":"J Neurol"},{"key":"2238_CR10","doi-asserted-by":"publisher","first-page":"104947","DOI":"10.1016\/j.compbiomed.2021.104947","volume":"139","author":"MS Tan","year":"2021","unstructured":"Tan MS, Cheah P-L, Chin A-V, Looi L-M, Chang S-W. A review on omics-based biomarkers discovery for Alzheimer\u2019s disease from the bioinformatics perspectives: statistical approach vs machine learning approach. Comput Biol Med. 2021;139:104947.","journal-title":"Comput Biol Med"},{"issue":"1","key":"2238_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13195-022-01055-y","volume":"14","author":"C Abdelnour","year":"2022","unstructured":"Abdelnour C, Agosta F, Bozzali M, Foug\u00e8re B, Iwata A, Nilforooshan R, Takada LT, Vi\u00f1uela F, Traber M. Perspectives and challenges in patient stratification in Alzheimer\u2019s disease. Alzheimers Res Ther. 2022;14(1):1\u201312.","journal-title":"Alzheimers Res Ther"},{"issue":"2","key":"2238_CR12","doi-asserted-by":"publisher","first-page":"729","DOI":"10.3233\/JAD-201447","volume":"81","author":"JF Mart\u00ednez-Florez","year":"2021","unstructured":"Mart\u00ednez-Florez JF, Osorio JD, Cediel JC, Rivas JC, Granados-S\u00e1nchez AM, L\u00f3pez-Pel\u00e1ez J, Jaramillo T, Cardona JF. Short-term memory binding distinguishing amnestic mild cognitive impairment from healthy aging: a machine learning study. J Alzheimers Dis. 2021;81(2):729\u201342.","journal-title":"J Alzheimers Dis"},{"issue":"4","key":"2238_CR13","doi-asserted-by":"publisher","first-page":"453","DOI":"10.3390\/brainsci11040453","volume":"11","author":"M Song","year":"2021","unstructured":"Song M, Jung H, Lee S, Kim D, Ahn M. Diagnostic classification and biomarker identification of Alzheimer\u2019s disease with random forest algorithm. Brain Sci. 2021;11(4):453.","journal-title":"Brain Sci"},{"issue":"9","key":"2238_CR14","doi-asserted-by":"publisher","first-page":"2737","DOI":"10.1007\/s11517-022-02630-z","volume":"60","author":"F Garc\u00eda-Gutierrez","year":"2022","unstructured":"Garc\u00eda-Gutierrez F, D\u00edaz-\u00c1lvarez J, Matias-Guiu JA, Pytel V, Mat\u00edas-Guiu J, Cabrera-Mart\u00edn MN, Ayala JL. GA-MADRID: design and validation of a machine learning tool for the diagnosis of Alzheimer\u2019s disease and frontotemporal dementia using genetic algorithms. Med Biol Eng Comput. 2022;60(9):2737\u201356.","journal-title":"Med Biol Eng Comput"},{"key":"2238_CR15","doi-asserted-by":"publisher","first-page":"104935","DOI":"10.1016\/j.compbiomed.2021.104935","volume":"138","author":"W Liang","year":"2021","unstructured":"Liang W, Zhang K, Cao P, Liu X, Yang J, Zaiane O. Rethinking modeling Alzheimer\u2019s disease progression from a multi-task learning perspective with deep recurrent neural network. Comput Biol Med. 2021;138:104935.","journal-title":"Comput Biol Med"},{"key":"2238_CR16","doi-asserted-by":"publisher","first-page":"103089","DOI":"10.1016\/j.jbi.2018.12.003","volume":"90","author":"S Fotouhi","year":"2019","unstructured":"Fotouhi S, Asadi S, Kattan MW. A comprehensive data level analysis for cancer diagnosis on imbalanced data. J Biomed Inform. 2019;90:103089.","journal-title":"J Biomed Inform"},{"key":"2238_CR17","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining: 2016; 2016: 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"key":"2238_CR18","unstructured":"Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 2017, 30."},{"issue":"3","key":"2238_CR19","doi-asserted-by":"publisher","first-page":"e212240","DOI":"10.1001\/jamanetworkopen.2021.2240","volume":"4","author":"B Xue","year":"2021","unstructured":"Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, Kannampallil T, Abraham J. Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications. JAMA Netw Open. 2021;4(3):e212240\u20130.","journal-title":"JAMA Netw Open"},{"key":"2238_CR20","doi-asserted-by":"publisher","first-page":"77","DOI":"10.3389\/fnagi.2020.00077","volume":"12","author":"W Lin","year":"2020","unstructured":"Lin W, Gao Q, Yuan J, Chen Z, Feng C, Chen W, Du M, Tong T. Predicting Alzheimer\u2019s disease conversion from mild cognitive impairment using an extreme learning machine-based grading method with multimodal data. Front Aging Neurosci. 2020;12:77.","journal-title":"Front Aging Neurosci"},{"issue":"5","key":"2238_CR21","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1007\/s10278-019-00265-5","volume":"33","author":"AB Tufail","year":"2020","unstructured":"Tufail AB, Ma Y-K, Zhang Q-N. Binary classification of Alzheimer\u2019s disease using sMRI imaging modality and deep learning. J Digit Imaging. 2020;33(5):1073\u201390.","journal-title":"J Digit Imaging"},{"key":"2238_CR22","doi-asserted-by":"publisher","first-page":"105657","DOI":"10.1016\/j.compbiomed.2022.105657","volume":"146","author":"S Akter","year":"2022","unstructured":"Akter S, Das D, Haque RU, Tonmoy MIQ, Hasan MR, Mahjabeen S, Ahmed M. AD-CovNet: an exploratory analysis using a hybrid deep learning model to handle data imbalance, predict fatality, and risk factors in Alzheimer\u2019s patients with COVID-19. Comput Biol Med. 2022;146:105657.","journal-title":"Comput Biol Med"},{"key":"2238_CR23","doi-asserted-by":"publisher","first-page":"104478","DOI":"10.1016\/j.compbiomed.2021.104478","volume":"134","author":"W Lin","year":"2021","unstructured":"Lin W, Gao Q, Du M, Chen W, Tong T. Multiclass diagnosis of stages of Alzheimer\u2019s disease using linear discriminant analysis scoring for multimodal data. Comput Biol Med. 2021;134:104478.","journal-title":"Comput Biol Med"},{"key":"2238_CR24","doi-asserted-by":"publisher","first-page":"104537","DOI":"10.1016\/j.compbiomed.2021.104537","volume":"134","author":"A Ebrahimi","year":"2021","unstructured":"Ebrahimi A, Luo S, Chiong R, Initiative AsDN. Deep sequence modelling for Alzheimer\u2019s disease detection using MRI. Comput Biol Med. 2021;134:104537.","journal-title":"Comput Biol Med"},{"key":"2238_CR25","doi-asserted-by":"publisher","first-page":"101694","DOI":"10.1016\/j.media.2020.101694","volume":"63","author":"J Wen","year":"2020","unstructured":"Wen J, Thibeau-Sutre E, Diaz-Melo M, Samper-Gonz\u00e1lez J, Routier A, Bottani S, Dormont D, Durrleman S, Burgos N, Colliot O. Convolutional neural networks for classification of Alzheimer\u2019s disease: overview and reproducible evaluation. Med Image Anal. 2020;63:101694.","journal-title":"Med Image Anal"},{"key":"2238_CR26","doi-asserted-by":"publisher","first-page":"101713","DOI":"10.1016\/j.compmedimag.2020.101713","volume":"81","author":"S Basheera","year":"2020","unstructured":"Basheera S, Ram MSS. A novel CNN based Alzheimer\u2019s disease classification using hybrid enhanced ICA segmented gray matter of MRI. Comput Med Imaging Graph. 2020;81:101713.","journal-title":"Comput Med Imaging Graph"},{"key":"2238_CR27","doi-asserted-by":"publisher","first-page":"626154","DOI":"10.3389\/fnins.2020.626154","volume":"14","author":"J Hu","year":"2021","unstructured":"Hu J, Qing Z, Liu R, Zhang X, Lv P, Wang M, Wang Y, He K, Gao Y, Zhang B. Deep learning-based classification and voxel-based visualization of frontotemporal dementia and Alzheimer\u2019s disease. Front Neurosci. 2021;14:626154.","journal-title":"Front Neurosci"},{"issue":"1","key":"2238_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12967-020-02620-5","volume":"18","author":"N Hou","year":"2020","unstructured":"Hou N, Li M, He L, Xie B, Wang L, Zhang R, Yu Y, Sun X, Pan Z, Wang K. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J Transl Med. 2020;18(1):1\u201314.","journal-title":"J Transl Med"},{"key":"2238_CR29","doi-asserted-by":"publisher","first-page":"585029","DOI":"10.3389\/fgene.2020.585029","volume":"11","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Feng T, Wang S, Dong R, Yang J, Su J, Wang B. A novel XGBoost method to identify cancer tissue-of-origin based on copy number variations. Front Genet. 2020;11:585029.","journal-title":"Front Genet"},{"issue":"4","key":"2238_CR30","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1093\/bioinformatics\/btz734","volume":"36","author":"B Yu","year":"2020","unstructured":"Yu B, Qiu W, Chen C, Ma A, Jiang J, Zhou H, Ma Q. SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting. Bioinformatics. 2020;36(4):1074\u201381.","journal-title":"Bioinformatics"},{"key":"2238_CR31","unstructured":"Lundberg SM, Erion GG, Lee S-I. Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:180203888 2018."},{"issue":"1","key":"2238_CR32","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee S-I. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2(1):56\u201367.","journal-title":"Nat Mach Intell"},{"key":"2238_CR33","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.ins.2021.07.019","volume":"577","author":"J Sanz","year":"2021","unstructured":"Sanz J, Sesma-Sara M, Bustince H. A fuzzy association rule-based classifier for imbalanced classification problems. Inf Sci. 2021;577:265\u201379.","journal-title":"Inf Sci"},{"issue":"3","key":"2238_CR34","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1177\/0269881120972331","volume":"35","author":"C-H Chang","year":"2021","unstructured":"Chang C-H, Lin C-H, Liu C-Y, Huang C-S, Chen S-J, Lin W-C, Yang H-T, Lane H-Y. Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer\u2019s disease: machine learning approaches. J Psychopharmacol. 2021;35(3):265\u201372.","journal-title":"J Psychopharmacol"},{"issue":"12","key":"2238_CR35","doi-asserted-by":"publisher","first-page":"2206","DOI":"10.1111\/cns.13963","volume":"28","author":"X Wang","year":"2022","unstructured":"Wang X, Jiao B, Liu H, Wang Y, Hao X, Zhu Y, Xu B, Xu H, Zhang S, Jia X. Machine learning based on Optical Coherence Tomography images as a diagnostic tool for Alzheimer\u2019s disease. CNS Neurosci Ther. 2022;28(12):2206\u201317.","journal-title":"CNS Neurosci Ther"},{"issue":"1","key":"2238_CR36","doi-asserted-by":"publisher","first-page":"37","DOI":"10.31083\/j.fbl2701037","volume":"27","author":"J Zhou","year":"2022","unstructured":"Zhou J, Qiu Y, Liu X, Xie Z, Lv S, Peng Y, Li X. Annotating whole genome variants and constructing a multi-classifier based on samples of ADNI. Front Biosci (Landmark Ed). 2022;27(1):37.","journal-title":"Front Biosci (Landmark Ed)"},{"key":"2238_CR37","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.ejrad.2019.03.010","volume":"115","author":"X Min","year":"2019","unstructured":"Min X, Li M, Dong D, Feng Z, Zhang P, Ke Z, You H, Han F, Ma H, Tian J. Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: cross-validation of a machine learning method. Eur J Radiol. 2019;115:16\u201321.","journal-title":"Eur J Radiol"},{"issue":"1","key":"2238_CR38","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1016\/j.bbe.2019.12.003","volume":"40","author":"J Peng","year":"2020","unstructured":"Peng J, Hao D, Yang L, Du M, Song X, Jiang H, Zhang Y, Zheng D. Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random forest. Biocybern Biomed Eng. 2020;40(1):352\u201362.","journal-title":"Biocybern Biomed Eng"},{"issue":"1","key":"2238_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-04835-6","volume":"12","author":"V Rupapara","year":"2022","unstructured":"Rupapara V, Rustam F, Aljedaani W, Shahzad HF, Lee E, Ashraf I. Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model. Sci Rep. 2022;12(1):1\u201315.","journal-title":"Sci Rep"},{"issue":"24","key":"2238_CR40","doi-asserted-by":"publisher","first-page":"7474","DOI":"10.3390\/molecules26247474","volume":"26","author":"J Song","year":"2021","unstructured":"Song J, Xu Z, Cao L, Wang M, Hou Y, Li K. The Discovery of New Drug-Target interactions for breast Cancer Treatment. Molecules. 2021;26(24):7474.","journal-title":"Molecules"},{"issue":"2","key":"2238_CR41","first-page":"87","volume":"7","author":"N Vinutha","year":"2020","unstructured":"Vinutha N, Pattar S, Sharma S, Shenoy P, Venugopal K. A machine learning framework for assessment of cognitive and functional impairments in Alzheimer\u2019s disease: data preprocessing and analysis. J Prev Alzheimers Dis. 2020;7(2):87\u201394.","journal-title":"J Prev Alzheimers Dis"},{"issue":"1","key":"2238_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-10202-2","volume":"12","author":"B Bogdanovic","year":"2022","unstructured":"Bogdanovic B, Eftimov T, Simjanoska M. In-depth insights into Alzheimer\u2019s disease by using explainable machine learning approach. Sci Rep. 2022;12(1):1\u201326.","journal-title":"Sci Rep"},{"key":"2238_CR43","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.neuroimage.2013.10.005","volume":"87","author":"R Dubey","year":"2014","unstructured":"Dubey R, Zhou J, Wang Y, Thompson PM, Ye J, Initiative AsDN. Analysis of sampling techniques for imbalanced data: an n = 648 ADNI study. NeuroImage. 2014;87:220\u201341.","journal-title":"NeuroImage"},{"key":"2238_CR44","doi-asserted-by":"crossref","unstructured":"Santos MS, Soares JP, Abreu PH, Araujo H, Santos J. Cross-validation for imbalanced datasets: avoiding overoptimistic and overfitting approaches [research frontier]. ieee ComputatioNal iNtelligeNCe magaziNe 2018, 13(4):59\u201376.","DOI":"10.1109\/MCI.2018.2866730"},{"issue":"1","key":"2238_CR45","first-page":"e12042","volume":"12","author":"E Tsoy","year":"2020","unstructured":"Tsoy E, Erlhoff SJ, Goode CA, Dorsman KA, Kanjanapong S, Lindbergh CA, La Joie R, Strom A, Rabinovici GD, Lanata SC. BHA-CS: a novel cognitive composite for Alzheimer\u2019s disease and related disorders. Alzheimers Dement (Amst). 2020;12(1):e12042.","journal-title":"Alzheimers Dement (Amst)"},{"issue":"1","key":"2238_CR46","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1093\/brain\/awz348","volume":"143","author":"D Younan","year":"2020","unstructured":"Younan D, Petkus AJ, Widaman KF, Wang X, Casanova R, Espeland MA, Gatz M, Henderson VW, Manson JE, Rapp SR. Particulate matter and episodic memory decline mediated by early neuroanatomic biomarkers of Alzheimer\u2019s disease. Brain. 2020;143(1):289\u2013302.","journal-title":"Brain"},{"issue":"2","key":"2238_CR47","doi-asserted-by":"publisher","first-page":"737","DOI":"10.3233\/JPD-202390","volume":"11","author":"J Gallagher","year":"2021","unstructured":"Gallagher J, Rick J, Xie SX, Martinez-Martin P, Mamikonyan E, Chen-Plotkin A, Dahodwala N, Morley J, Duda JE, Trojanowski JQ. Psychometric Properties of the clinical dementia rating Scale Sum of Boxes in Parkinson\u2019s Disease. J Parkinsons Dis. 2021;11(2):737\u201345.","journal-title":"J Parkinsons Dis"},{"key":"2238_CR48","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1192\/bjp.140.6.566","volume":"140","author":"CP Hughes","year":"1982","unstructured":"Hughes CP, Berg L, Danziger WL, Coben LA, Martin RL. A new clinical scale for the staging of dementia. Br J Psychiatry. 1982;140:566\u201372.","journal-title":"Br J Psychiatry"},{"key":"2238_CR49","doi-asserted-by":"publisher","first-page":"1021792","DOI":"10.3389\/fnagi.2022.1021792","volume":"14","author":"RC Tzeng","year":"2022","unstructured":"Tzeng RC, Yang YW, Hsu KC, Chang HT, Chiu PY. Sum of boxes of the clinical dementia rating scale highly predicts conversion or reversion in predementia stages. Front Aging Neurosci. 2022;14:1021792.","journal-title":"Front Aging Neurosci"},{"issue":"11","key":"2238_CR50","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1176\/ajp.141.11.1356","volume":"141","author":"WG Rosen","year":"1984","unstructured":"Rosen WG, Mohs RC, Davis KL. A new rating scale for Alzheimer\u2019s disease. Am J Psychiatry. 1984;141(11):1356\u201364.","journal-title":"Am J Psychiatry"},{"issue":"2","key":"2238_CR51","doi-asserted-by":"publisher","first-page":"423","DOI":"10.3233\/JAD-170991","volume":"63","author":"JK Kueper","year":"2018","unstructured":"Kueper JK, Speechley M, Montero-Odasso M. The Alzheimer\u2019s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog): modifications and responsiveness in Pre-Dementia populations. Narrative Rev J Alzheimers Dis. 2018;63(2):423\u201344.","journal-title":"Narrative Rev J Alzheimers Dis"},{"issue":"1","key":"2238_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13195-016-0170-5","volume":"8","author":"J Podhorna","year":"2016","unstructured":"Podhorna J, Krahnke T, Shear M, Harrison JE. Alzheimer\u2019s Disease Neuroimaging Initiative: Alzheimer\u2019s Disease Assessment Scale-Cognitive subscale variants in mild cognitive impairment and mild Alzheimer\u2019s disease: change over time and the effect of enrichment strategies. Alzheimers Res Ther. 2016;8(1):1\u201313.","journal-title":"Alzheimers Res Ther"},{"key":"2238_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12041-021-01309-y","volume":"100","author":"E Fokuoh","year":"2021","unstructured":"Fokuoh E, Xiao D, Fang W, Liu Y, Lu Y, Wang K. Longitudinal analysis of APOE-\u025b4 genotype with the logical memory delayed recall score in Alzheimer\u2019s disease. J Genet. 2021;100:1\u20139.","journal-title":"J Genet"},{"issue":"3","key":"2238_CR54","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1111\/jnp.12235","volume":"15","author":"D Bruno","year":"2021","unstructured":"Bruno D, Mueller KD, Betthauser T, Chin N, Engelman CD, Christian B, Koscik RL, Johnson SC. Serial position effects in the logical memory test: loss of primacy predicts amyloid positivity. J Neuropsychol. 2021;15(3):448\u201361.","journal-title":"J Neuropsychol"},{"key":"2238_CR55","doi-asserted-by":"crossref","unstructured":"Zhang X, Wu Y, He Y, Ge X, Cui J, Han H, Luo Y, Liu L, Wang Z, Yu H. Metrological properties of neuropsychological tests for measuring cognitive change in individuals with prodromal Alzheimer\u2019s disease. Aging Ment Health 2021:1\u20139.","DOI":"10.1080\/13607863.2021.1966746"},{"key":"2238_CR56","doi-asserted-by":"crossref","unstructured":"Lombardi G, Crescioli G, Cavedo E, Lucenteforte E, Casazza G, Bellatorre AG, Lista C, Costantino G, Frisoni G, Virgili G. Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer\u2019s disease in people with mild cognitive impairment. Cochrane Database Syst Rev 2020(3).","DOI":"10.1002\/14651858.CD009628.pub2"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-023-02238-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-023-02238-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-023-02238-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T13:02:55Z","timestamp":1690290175000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-023-02238-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,25]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["2238"],"URL":"https:\/\/doi.org\/10.1186\/s12911-023-02238-9","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,25]]},"assertion":[{"value":"9 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to report.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The data used in this study were obtained from publicly available datasets, namely, the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) dataset and the National Alzheimer\u2019s Coordinating Center (NACC) dataset. The study procedures were approved by the relevant ethics committees at each participating site, and all participants provided informed consent prior to inclusion in the ADNI and NACC. For current information on the ADNI and NACC studies, please visit adni.loni.usc.edu and , respectively.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"137"}}