{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T02:39:34Z","timestamp":1789699174997,"version":"4.0.1"},"reference-count":64,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T00:00:00Z","timestamp":1661731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Early identification of Alzheimer\u2019s disease or mild cognitive impairment can help guide direct prevention and supportive treatments, improve outcomes, and reduce medical costs. Existing advanced diagnostic tools are mostly based on neuroimaging and suffer from certain problems in cost, reliability, repeatability, accessibility, ease of use, and clinical integration. To address these problems, we developed, evaluated, and implemented an early diagnostic tool using machine learning and non-imaging factors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and results<\/jats:title><jats:p>A total of 654 participants aged 65 or older from the Nursing Home in Hangzhou, China were identified. Information collected from these patients includes dementia status and 70 demographic, cognitive, socioeconomic, and clinical features. Logistic regression, support vector machine (SVM), neural network, random forest, extreme gradient boosting (XGBoost), least absolute shrinkage and selection operator (LASSO), and best subset models were trained, tuned, and internally validated using a novel double cross validation algorithm and multiple evaluation metrics. The trained models were also compared and externally validated using a separate dataset with 1,100 participants from four communities in Zhejiang Province, China. The model with the best performance was then identified and implemented online with a friendly user interface. For the nursing dataset, the top three models are the neural network (AUROC = 0.9435), XGBoost (AUROC = 0.9398), and SVM with the polynomial kernel (AUROC = 0.9213). With the community dataset, the best three models are the random forest (AUROC = 0.9259), SVM with linear kernel (AUROC = 0.9282), and SVM with polynomial kernel (AUROC = 0.9213). The F1 scores and area under the precision-recall curve showed that the SVMs, neural network, and random forest were robust on the unbalanced community dataset. Overall the SVM with the polynomial kernel was found to be the best model. The LASSO and best subset models identified 17 features most relevant to dementia prediction, mostly from cognitive test results and socioeconomic characteristics.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our non-imaging-based diagnostic tool can effectively predict dementia outcomes. The tool can be conveniently incorporated into clinical practice. Its online implementation allows zero barriers to its use, which enhances the disease\u2019s diagnosis, improves the quality of care, and reduces costs.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnagi.2022.945274","type":"journal-article","created":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T12:13:23Z","timestamp":1661516003000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Develop a diagnostic tool for dementia using machine learning and non-imaging features"],"prefix":"10.3389","volume":"14","author":[{"given":"Huan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Sheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanhu","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqing","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingqing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenmin","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenlei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Genxiang","family":"Mao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaogang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,8,29]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1109\/CBMS.2018.00067","article-title":"Classification of Alzheimer disease on imaging modalities with deep CNNs using cross-modal transfer learning","author":"Aderghal","year":"2018","journal-title":"2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)"},{"key":"B2","doi-asserted-by":"publisher","first-page":"3240","DOI":"10.1016\/j.eswa.2008.01.009","article-title":"Support vector machines combined with feature selection for breast cancer diagnosis.","volume":"36","author":"Akay","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"B3","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.jalz.2011.03.008","article-title":"The diagnosis of mild cognitive impairment due to Alzheimer\u2019s disease: Recommendations from the National Institute on Aging-Alzheimer\u2019s Association workgroups on diagnostic guidelines for Alzheimer\u2019s disease.","volume":"7","author":"Albert","year":"2011","journal-title":"Alzheimers Dement."},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.2478\/v10136-012-0031-x","author":"Amato","year":"2013","journal-title":"Artificial Neural Networks in Medical Diagnosis."},{"key":"B5","doi-asserted-by":"publisher","first-page":"607","DOI":"10.3233\/JAD-201580","article-title":"Impact of Social Isolation on People with Dementia and Their Family Caregivers.","volume":"81","author":"Azevedo","year":"2021","journal-title":"J. Alzheimers Dis."},{"key":"B6","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1053\/j.semnuclmed.2014.12.003","article-title":"PET\/MR in dementia and other neurodegenerative diseases.","volume":"45","author":"Barthel","year":"2015","journal-title":"Semin. Nucl. Med."},{"key":"B7","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1212\/WNL.0000000000007176","article-title":"Global & Community Health: Brief in-hospital cognitive screening anticipates complex admissions and may detect dementia.","volume":"92","author":"Bissig","year":"2019","journal-title":"Neurology"},{"key":"B8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests.","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"B9","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1016\/j.dadm.2018.07.003","article-title":"Evaluating combinations of diagnostic tests to discriminate different dementia types.","volume":"10","author":"Bruun","year":"2018","journal-title":"Alzheimers Dement."},{"key":"B10","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1159\/000093378","article-title":"Education and dementia: A meta-analytic study.","volume":"26","author":"Caama\u00f1o-Isorna","year":"2006","journal-title":"Neuroepidemiology"},{"key":"B11","first-page":"1239","article-title":"Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector","author":"Cai","year":"2016","journal-title":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)"},{"key":"B12","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"Xgboost: A scalable tree boosting system","author":"Chen","year":"2016","journal-title":"Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining"},{"key":"B13","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.compbiomed.2005.11.002","article-title":"A decision support system based on support vector machines for diagnosis of the heart valve diseases.","volume":"37","author":"Comak","year":"2007","journal-title":"Comput. Biol. Med."},{"key":"B14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compmedimag.2019.01.005","article-title":"RNN-based longitudinal analysis for diagnosis of Alzheimer\u2019s disease.","volume":"73","author":"Cui","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"key":"B15","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1148\/radiol.2018180958","article-title":"A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using (18)F-FDG PET of the Brain.","volume":"290","author":"Ding","year":"2019","journal-title":"Radiology"},{"key":"B16","doi-asserted-by":"publisher","first-page":"2259","DOI":"10.1109\/IJCNN.2017.7966129","article-title":"Deep learning of texture and structural features for multiclass Alzheimer\u2019s disease classification","author":"Dolph","year":"2017","journal-title":"2017 International Joint Conference on Neural Networks (IJCNN)"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1262","DOI":"10.1016\/S0140-6736(06)68542-5","article-title":"International Psychogeriatric Association Expert Conference on mild cognitive impairment. Mild cognitive impairment.","volume":"367","author":"Feldman","year":"2006","journal-title":"Lancet"},{"key":"B18","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting.","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"B19","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1056\/NEJM199608013350507","article-title":"Evaluation of dementia.","volume":"335","author":"Geldmacher","year":"1996","journal-title":"N. Engl. J. Med."},{"key":"B20","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.neuroimage.2012.09.065","article-title":"Random forest-based similarity measures for multi-modal classification of Alzheimer\u2019s disease.","volume":"65","author":"Gray","year":"2013","journal-title":"NeuroImage"},{"key":"B21","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1001\/archneur.61.1.59","article-title":"Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials.","volume":"61","author":"Grundman","year":"2004","journal-title":"Arch. Neurol."},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21606-5","author":"Hastie","year":"2001","journal-title":"The Elements of Statistical Learning. Springer Series in Statistics."},{"key":"B23","article-title":"Extended comparisons of best subset selection, forward stepwise selection, and the lasso.","author":"Hastie","year":"2017","journal-title":"arXiv."},{"key":"B24","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.1109\/BIBM.2017.8217822","article-title":"Towards Alzheimer\u2019s disease classification through transfer learning","author":"Hon","year":"2017","journal-title":"2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1002\/9781118548387","author":"Hosmer","year":"2013","journal-title":"Applied Logistic Regression."},{"key":"B26","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1109\/ICIP.2016.7532332","article-title":"Alzheimer\u2019s disease diagnostics by adaptation of 3D convolutional network","author":"Hosseini-Asl","year":"2016","journal-title":"2016 IEEE International Conference on Image Processing (ICIP)"},{"key":"B27","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1136\/jnnp-2018-318212","article-title":"Models for predicting risk of dementia: A systematic review.","volume":"90","author":"Hou","year":"2019","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"B28","doi-asserted-by":"publisher","first-page":"2030","DOI":"10.3174\/ajnr.A3695","article-title":"PET approaches for diagnosis of dementia.","volume":"35","author":"Ishii","year":"2014","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"B29","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-319-70772-3_20","article-title":"A novel deep learning based multi-class classification method for Alzheimer\u2019s disease detection using brain MRI data","author":"Islam","year":"2017","journal-title":"International Conference on Brain Informatics"},{"key":"B30","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1038\/s41591-020-0755-1","article-title":"Plasma P-tau181 in Alzheimer\u2019s disease: Relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer\u2019s dementia.","volume":"26","author":"Janelidze","year":"2020","journal-title":"Nat. Med."},{"key":"B31","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1109\/ACII.2013.47","article-title":"Facing imbalanced data\u2013recommendations for the use of performance metrics","author":"Jeni","year":"2013","journal-title":"2013 Humaine Association Conference on Affective Computing and Intelligent Interaction"},{"key":"B32","doi-asserted-by":"publisher","first-page":"220","DOI":"10.3389\/fnagi.2019.00220","article-title":"Deep Learning in Alzheimer\u2019s Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data.","volume":"11","author":"Jo","year":"2019","journal-title":"Front. Aging Neurosci."},{"key":"B33","doi-asserted-by":"publisher","first-page":"72","DOI":"10.3171\/jns.2007.106.1.72","article-title":"The diagnostic utility of brain biopsy procedures in patients with rapidly deteriorating neurological conditions or dementia.","volume":"106","author":"Josephson","year":"2007","journal-title":"J. Neurosurg."},{"key":"B34","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1186\/1471-2105-15-276","article-title":"Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks.","volume":"15","author":"Kane","year":"2014","journal-title":"BMC Bioinformatics"},{"key":"B35","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/1472-6947-11-51","article-title":"Predicting disease risks from highly imbalanced data using random forest.","volume":"11","author":"Khalilia","year":"2011","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"B36","article-title":"3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies.","author":"Khvostikov","year":"2018","journal-title":"arXiv"},{"key":"B37","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/1758-2946-6-10","article-title":"Cross-validation pitfalls when selecting and assessing regression and classification models.","volume":"6","author":"Krstajic","year":"2014","journal-title":"J. Cheminform."},{"key":"B38","doi-asserted-by":"publisher","first-page":"1952","DOI":"10.1038\/s41598-018-37769-z","article-title":"Predicting Alzheimer\u2019s disease progression using multi-modal deep learning approach.","volume":"9","author":"Lee","year":"2019","journal-title":"Sci. Rep."},{"key":"B39","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1002\/ana.22100","article-title":"Amyloid and tau proteins in cortical brain biopsy and Alzheimer\u2019s disease.","volume":"68","author":"Leinonen","year":"2010","journal-title":"Ann. Neurol."},{"key":"B40","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.cell.2019.09.001","article-title":"Alzheimer Disease: An Update on Pathobiology and Treatment Strategies.","volume":"179","author":"Long","year":"2019","journal-title":"Cell"},{"key":"B41","first-page":"431","article-title":"Understanding variable importances in forests of randomized trees.","volume":"26","author":"Louppe","year":"2013","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"B42","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1186\/1756-0500-4-299","article-title":"Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests.","volume":"4","author":"Maroco","year":"2011","journal-title":"BMC Res. Notes"},{"key":"B43","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.jalz.2011.03.005","article-title":"The diagnosis of dementia due to Alzheimer\u2019s disease: Recommendations from the National Institute on Aging-Alzheimer\u2019s Association workgroups on diagnostic guidelines for Alzheimer\u2019s disease.","volume":"7","author":"McKhann","year":"2011","journal-title":"Alzheimers Dement."},{"key":"B44","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1056\/NEJMoa0902234","article-title":"The clinical course of advanced dementia.","volume":"361","author":"Mitchell","year":"2009","journal-title":"N. Engl. J. Med."},{"key":"B45","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1007\/s40336-013-0026-y","article-title":"Glucose metabolism in normal aging and Alzheimer\u2019s disease: Methodological and physiological considerations for PET studies.","volume":"1","author":"Mosconi","year":"2013","journal-title":"Clin. Transl. Imaging"},{"key":"B46","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1016\/j.dadm.2018.07.004","article-title":"Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review.","volume":"10","author":"Pellegrini","year":"2018","journal-title":"Alzheimers Dement."},{"key":"B47","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1001\/archneur.56.3.303","article-title":"Mild cognitive impairment: Clinical characterization and outcome.","volume":"56","author":"Petersen","year":"1999","journal-title":"Arch. Neurol."},{"key":"B48","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1001\/jama.1982.03330030039022","article-title":"The impact of dementia on the family.","volume":"248","author":"Rabins","year":"1982","journal-title":"JAMA"},{"key":"B49","doi-asserted-by":"publisher","first-page":"e0118432","DOI":"10.1371\/journal.pone.0118432","article-title":"The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets.","volume":"10","author":"Saito","year":"2015","journal-title":"PLoS One"},{"key":"B50","article-title":"Classification of alzheimer\u2019s disease using fmri data and deep learning convolutional neural networks.","author":"Sarraf","year":"2016","journal-title":"arXiv"},{"key":"B51","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1126\/science.aay5188","article-title":"Early network dysfunction in Alzheimer\u2019s disease.","volume":"365","author":"Selkoe","year":"2019","journal-title":"Science"},{"key":"B52","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/BFb0029438","article-title":"Detecting very early stages of dementia from normal aging with machine learning methods","author":"Shankle","year":"1997","journal-title":"Conference on Artificial Intelligence in Medicine in Europe"},{"key":"B53","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1097\/WAD.0b013e318211c83c","article-title":"The relationship between education and dementia an updated systematic review.","volume":"25","author":"Sharp","year":"2011","journal-title":"Alzheimer Dis. Assoc. Disord."},{"key":"B54","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1177\/1756285612455733","article-title":"Assessment scales in dementia.","volume":"5","author":"Sheehan","year":"2012","journal-title":"Ther. Adv. Neurol. Disord."},{"key":"B55","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1016\/j.patcog.2016.09.032","article-title":"Nonlinear feature transformation and deep fusion for Alzheimer\u2019s Disease staging analysis.","volume":"63","author":"Shi","year":"2017","journal-title":"Pattern Recognize."},{"key":"B56","doi-asserted-by":"publisher","first-page":"W76","DOI":"10.2214\/AJR.13.12363","article-title":"Brain FDG PET and the diagnosis of dementia.","volume":"204","author":"Shivamurthy","year":"2015","journal-title":"AJR Am. J. Roentgenol."},{"key":"B57","article-title":"Gaussian process optimization in the bandit setting: No regret and experimental design.","author":"Srinivas","year":"2009","journal-title":"Proceedings of the 27th International Conference on Machine Learning"},{"key":"B58","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso.","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R Stat. Soc."},{"key":"B59","author":"Vapnik","year":"2013","journal-title":"The Nature of Statistical Learning Theory."},{"key":"B60","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1186\/1471-2105-7-91","article-title":"Bias in error estimation when using cross-validation for model selection.","volume":"7","author":"Varma","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"B61","doi-asserted-by":"publisher","first-page":"2016","DOI":"10.1093\/brain\/awh543","article-title":"Brain biopsy in dementia.","volume":"128","author":"Warren","year":"2005","journal-title":"Brain"},{"key":"B62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v094.i04","article-title":"Bess: An r package for best subset selection in linear, logistic and CoxPH models.","volume":"94","author":"Wen","year":"2017","journal-title":"J. Stat. Softw."},{"key":"B63","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/1472-6947-10-16","article-title":"Application of support vector machine modeling for prediction of common diseases: The case of diabetes and pre-diabetes.","volume":"10","author":"Yu","year":"2010","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"B64","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.eswa.2017.04.003","article-title":"An up-to-date comparison of state-of-the-art classification algorithms.","volume":"82","author":"Zhang","year":"2017","journal-title":"Expert Syst. Appl."}],"container-title":["Frontiers in Aging Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2022.945274\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T12:13:35Z","timestamp":1661516015000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2022.945274\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,29]]},"references-count":64,"alternative-id":["10.3389\/fnagi.2022.945274"],"URL":"https:\/\/doi.org\/10.3389\/fnagi.2022.945274","relation":{},"ISSN":["1663-4365"],"issn-type":[{"value":"1663-4365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,29]]},"article-number":"945274"}}