{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T05:54:30Z","timestamp":1771566870658,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T00:00:00Z","timestamp":1598227200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T00:00:00Z","timestamp":1598227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Russian scientific fund","award":["19-11-00326"],"award-info":[{"award-number":["19-11-00326"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n<jats:title>Background<\/jats:title>\n<jats:p>Methods of data mining and analytics can be efficiently applied in medicine to develop models that use patient-specific data to predict the development of diabetic polyneuropathy. However, there is room for improvement in the accuracy of predictive models. Existing studies of diabetes polyneuropathy considered a limited number of predictors in one study to enable a comparison of efficiency of different machine learning methods with different predictors to find the most efficient one. The purpose of this study is the implementation of machine learning methods for identifying the risk of diabetes polyneuropathy based on structured electronic medical records collected in databases of medical information systems.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Methods<\/jats:title>\n<jats:p>For the purposes of our study, we developed a structured procedure for predictive modelling, which includes data extraction and preprocessing, model adjustment and performance assessment, selection of the best models and interpretation of results. The dataset contained a total number of 238,590 laboratory records. Each record 27 laboratory tests, age, gender and presence of retinopathy or nephropathy). The records included information about 5846 patients with diabetes. Diagnosis served as a source of information about the target class values for classification.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>It was discovered that inclusion of two expressions, namely \u201cnephropathy\u201d and \u201cretinopathy\u201d allows to increase the performance, achieving up to 79.82% precision, 81.52% recall, 80.64% F1 score, 82.61% accuracy, and 89.88% AUC using the neural network classifier. Additionally, different models showed different results in terms of interpretation significance: random forest confirmed that the most important risk factor for polyneuropathy is the increased neutrophil level, meaning the presence of inflammation in the body. Linear models showed linear dependencies of the presence of polyneuropathy on blood glucose levels, which is confirmed by the clinical interpretation of the importance of blood glucose control.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusion<\/jats:title>\n<jats:p>Depending on whether one needs to identify pathophysiological mechanisms for one\u2019s prospective study or identify early or late predictors, the choice of model will vary. In comparison with the previous studies, our research makes a comprehensive comparison of different decisions using a large and well-structured dataset applied to different decision support tasks.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12911-020-01215-w","type":"journal-article","created":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T11:03:02Z","timestamp":1598266982000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Identification of risk factors for patients with diabetes: diabetic polyneuropathy case study"],"prefix":"10.1186","volume":"20","author":[{"given":"Oleg","family":"Metsker","sequence":"first","affiliation":[]},{"given":"Kirill","family":"Magoev","sequence":"additional","affiliation":[]},{"given":"Alexey","family":"Yakovlev","sequence":"additional","affiliation":[]},{"given":"Stanislav","family":"Yanishevskiy","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6231-8036","authenticated-orcid":false,"given":"Georgy","family":"Kopanitsa","sequence":"additional","affiliation":[]},{"given":"Sergey","family":"Kovalchuk","sequence":"additional","affiliation":[]},{"given":"Valeria V.","family":"Krzhizhanovskaya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,24]]},"reference":[{"issue":"4","key":"1215_CR1","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1055\/s-0035-1558972","volume":"35","author":"A Izenberg","year":"2015","unstructured":"Izenberg A, Perkins BA, Bril V. Diabetic neuropathies. Semin Neurol. 2015;35(4):424\u201330.","journal-title":"Semin Neurol"},{"issue":"2","key":"1215_CR2","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s11940-011-0113-1","volume":"13","author":"L Zilliox","year":"2011","unstructured":"Zilliox L, Russell JW. Treatment of diabetic sensory polyneuropathy. Curr Treatment Options Neurol. 2011;13(2):143\u201359.","journal-title":"Curr Treatment Options Neurol"},{"issue":"7","key":"1215_CR3","doi-asserted-by":"publisher","first-page":"1634","DOI":"10.2337\/db08-1771","volume":"58","author":"TD Wiggin","year":"2009","unstructured":"Wiggin TD, Sullivan KA, Pop-Busui R, Amato A, Sima AAF, Feldman EL. Elevated triglycerides correlate with progression of diabetic neuropathy. Diabetes. 2009;58(7):1634\u201340.","journal-title":"Diabetes"},{"key":"1215_CR4","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.csbj.2016.12.005","volume":"15","author":"I Kavakiotis","year":"2017","unstructured":"Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J. 2017;15:104\u201316.","journal-title":"Comput Struct Biotechnol J"},{"key":"1215_CR5","doi-asserted-by":"publisher","first-page":"515","DOI":"10.3389\/fgene.2018.00515","volume":"9","author":"Q Zou","year":"2018","unstructured":"Zou Q, Qu K, Luo Y, Yin D, Ju Y, Tang H. Predicting diabetes mellitus with machine learning techniques. Front Genet. 2018;9:515.","journal-title":"Front Genet"},{"issue":"4","key":"1215_CR6","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1089\/big.2015.0020","volume":"3","author":"N Razavian","year":"2015","unstructured":"Razavian N, Blecker S, Schmidt AM, Smith-McLallen A, Nigam S, Sontag D. Population-level prediction of type 2 diabetes from claims data and analysis of risk factors. Big Data. 2015;3(4):277\u201387.","journal-title":"Big Data"},{"issue":"1","key":"1215_CR7","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1089\/big.2015.0029","volume":"4","author":"W Oh","year":"2016","unstructured":"Oh W, et al. Type 2 diabetes mellitus trajectories and associated risks. Big Data. 2016;4(1):25\u201330.","journal-title":"Big Data"},{"key":"1215_CR8","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1007\/978-1-4899-7687-1_810","volume-title":"Encyclopedia of machine learning and data mining","author":"X Zhang","year":"2017","unstructured":"Zhang X. Support vector machines. In: Encyclopedia of machine learning and data mining. Boston, MA: Springer US; 2017. p. 1214\u201320."},{"key":"1215_CR9","first-page":"65","volume-title":"Encyclopedia of machine learning and data mining","author":"Artificial Neural Networks","year":"2017","unstructured":"Artificial Neural Networks. Encyclopedia of machine learning and data mining. Boston: Springer US; 2017. p. 65\u20136."},{"key":"1215_CR10","first-page":"330","volume-title":"Encyclopedia of machine learning and data mining","author":"J F\u00fcrnkranz","year":"2017","unstructured":"F\u00fcrnkranz J. Decision tree. In: Encyclopedia of machine learning and data mining. Boston: Springer US; 2017. p. 330\u20135."},{"key":"1215_CR11","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.jbi.2015.12.001","volume":"59","author":"S Bashir","year":"2016","unstructured":"Bashir S, Qamar U, Khan FH. IntelliHealth: a medical decision support application using a novel weighted multi-layer classifier ensemble framework. J Biomed Inform. 2016;59:185\u2013200.","journal-title":"J Biomed Inform"},{"issue":"3","key":"1215_CR12","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/j.cmpb.2011.03.018","volume":"104","author":"A Ozcift","year":"2011","unstructured":"Ozcift A, Gulten A. Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Comput Methods Programs Biomed. 2011;104(3):443\u201351.","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"1215_CR13","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1177\/1932296814554260","volume":"9","author":"B Sudharsan","year":"2015","unstructured":"Sudharsan B, Peeples M, Shomali M. Hypoglycemia prediction using machine learning models for patients with type 2 diabetes. J Diabetes Sci Technol. 2015;9(1):86\u201390.","journal-title":"J Diabetes Sci Technol"},{"issue":"2","key":"1215_CR14","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/S0168-8227(01)00278-9","volume":"54","author":"D Olaleye","year":"2001","unstructured":"Olaleye D, Perkins BA, Bril V. Evaluation of three screening tests and a risk assessment model for diagnosing peripheral neuropathy in the diabetes clinic. Diabetes Res Clin Pract. 2001;54(2):115\u201328.","journal-title":"Diabetes Res Clin Pract"},{"issue":"5","key":"1215_CR15","first-page":"851","volume":"125","author":"CP Li","year":"2012","unstructured":"Li CP, et al. Performance comparison between logistic regression, decision trees, and multilayer perceptron in predicting peripheral neuropathy in type 2 diabetes mellitus. Chin Med J (Engl). 2012;125(5):851\u20137.","journal-title":"Chin Med J (Engl)"},{"key":"1215_CR16","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1177\/1932296817706375","volume":"12","author":"A Dagliati","year":"2018","unstructured":"Dagliati A, et al. Machine learning methods to predict diabetes complications. J Diabetes Sci Technol. 2018;12:295\u2013302.","journal-title":"J Diabetes Sci Technol"},{"key":"1215_CR17","doi-asserted-by":"publisher","first-page":"S5","DOI":"10.1186\/1471-2105-16-S1-S5","volume":"16","author":"G-M Huang","year":"2015","unstructured":"Huang G-M, Huang K-Y, Lee T-Y, Weng J. An interpretable rule-based diagnostic classification of diabetic nephropathy among type 2 diabetes patients. BMC Bioinformatics. 2015;16:S5.","journal-title":"BMC Bioinformatics"},{"issue":"4","key":"1215_CR18","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/j.jdiacomp.2015.03.001","volume":"29","author":"V Lagani","year":"2015","unstructured":"Lagani V, et al. Development and validation of risk assessment models for diabetes-related complications based on the DCCT\/EDIC data. J Diabetes Complications. 2015;29(4):479\u201387.","journal-title":"J Diabetes Complications"},{"issue":"5","key":"1215_CR19","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1016\/j.jdiacomp.2015.03.011","volume":"29","author":"V Lagani","year":"2015","unstructured":"Lagani V, et al. Realization of a service for the long-term risk assessment of diabetes-related complications. J Diabetes Complications. 2015;29(5):691\u20138.","journal-title":"J Diabetes Complications"},{"key":"1215_CR20","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"1215_CR21","doi-asserted-by":"publisher","first-page":"8019232","DOI":"10.1155\/2018\/8019232","volume":"2018","author":"FHM Oliveira","year":"2018","unstructured":"Oliveira FHM, MacHado ARP, Andrade AO. On the use of t -distributed stochastic neighbor embedding for data visualization and classification of individuals with Parkinson\u2019s disease. Comput Math Methods Med. 2018;2018:8019232.","journal-title":"Comput Math Methods Med"},{"key":"1215_CR22","first-page":"818","volume-title":"Machine learning based text mining in electronic health records: cardiovascular patient cases","author":"S Sikorskiy","year":"2018","unstructured":"Sikorskiy S, Metsker O, Yakovlev A, Kovalchuk S. Machine learning based text mining in electronic health records: cardiovascular patient cases; 2018. p. 818\u201324."},{"key":"1215_CR23","first-page":"1135","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13\u201317, 2016","author":"MT Ribeiro","year":"2016","unstructured":"Ribeiro MT, Singh S, Guestrin C. Why Should I Trust You?\u2019: Explaining the Predictions of Any Classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13\u201317, 2016; 2016. p. 1135\u201344."},{"key":"1215_CR24","doi-asserted-by":"publisher","first-page":"1409","DOI":"10.1016\/0140-6736(91)92719-I","volume":"338","author":"J Martin","year":"1991","unstructured":"Martin J, Bath PM, Burr M. Influence of platelet size on outcome after myocardial infarction. Lancet. 1991;338:1409\u201311 Elsevier.","journal-title":"Lancet"},{"issue":"1","key":"1215_CR25","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1080\/09537100500220729","volume":"17","author":"E Coban","year":"2006","unstructured":"Coban E, Bostan F, Ozdogan M. The mean platelet volume in subjects with impaired fasting glucose. Platelets. 2006;17(1):67\u20139.","journal-title":"Platelets"},{"key":"1215_CR26","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.jdiacomp.2008.01.006","volume":"23","author":"R Demirtunc","year":"2009","unstructured":"Demirtunc R, Duman D, Basar M, et al. The relationship between glycemic control and platelet activity in type 2 diabetes mellitus. J Diabetes Complications. 2009;23:89\u201394 Elsevier.","journal-title":"J Diabetes Complications"},{"key":"1215_CR27","first-page":"1169","volume":"28","author":"D Ziegler","year":"2005","unstructured":"Ziegler D, Siekierka-Kleiser E, et al. Validation of a novel screening device (NeuroQuick) for quantitative assessment of small nerve fiber dysfunction as an early feature of diabetic polyneuropathy. Am Diabetes Assoc. 2005;28:1169\u201374.","journal-title":"Am Diabetes Assoc"},{"key":"1215_CR28","doi-asserted-by":"publisher","first-page":"dc190951","DOI":"10.2337\/dc19-0951","volume":"43","author":"EJH Lewis","year":"2020","unstructured":"Lewis EJH, et al. Rapid corneal nerve fiber loss: a marker of diabetic neuropathy onset and progression. Diabetes Care. 2020;43:dc190951.","journal-title":"Diabetes Care"},{"key":"1215_CR29","doi-asserted-by":"publisher","first-page":"e581","DOI":"10.5114\/pjr.2019.91439","volume":"84","author":"J Zakrzewski","year":"2019","unstructured":"Zakrzewski J, Zakrzewska K, Pluta K, Nowak O, Miloszewska-Paluch A. Ultrasound elastography in the evaluation of peripheral neuropathies: a systematic review of the literature. Polish J Radiol. 2019;84:e581\u201391.","journal-title":"Polish J Radiol"},{"key":"1215_CR30","doi-asserted-by":"publisher","unstructured":"Groener JB, Jende JME, Kurz FT, et al. Understanding diabetic neuropathy: from subclinical nerve lesions to severe nerve Fiber deficits: a cross-sectional study in patients with type 2 diabetes and healthy controls. Diabetes. 2020;69(3):436\u201347. https:\/\/doi.org\/10.2337\/db19-0197.","DOI":"10.2337\/db19-0197"},{"key":"1215_CR31","doi-asserted-by":"publisher","unstructured":"Kemp HI, Eliahoo J, Vase L, et al. Meta-analysis comparing placebo responses in clinical trials of painful HIV-associated sensory neuropathy and diabetic polyneuropathy. Scand J Pain. 2020;20(3):439\u201349. https:\/\/doi.org\/10.1515\/sjpain-2019-0152.","DOI":"10.1515\/sjpain-2019-0152"},{"issue":"4","key":"1215_CR32","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/s40265-020-01259-2","volume":"80","author":"U Alam","year":"2020","unstructured":"Alam U, Sloan G, Tesfaye S. Treating pain in diabetic neuropathy: current and developmental drugs. Drugs. 2020;80(4):363\u201384.","journal-title":"Drugs"},{"key":"1215_CR33","first-page":"1","volume-title":"Interpretable machine learning: definitions, methods, and applications","author":"WJ Murdoch","year":"2019","unstructured":"Murdoch WJ, Singh C, Kumbier K, Abbasi-Asl R, Yu B. Interpretable machine learning: definitions, methods, and applications; 2019. p. 1\u201311."},{"issue":"16","key":"1215_CR34","doi-asserted-by":"publisher","first-page":"2626","DOI":"10.3889\/oamjms.2019.454","volume":"7","author":"A Fitri","year":"2019","unstructured":"Fitri A, Sjahrir H, Bachtiar A, Ichwan M, Fitri FI, Rambe AS. Predictive model of diabetic polyneuropathy severity based on vitamin D level. Open Access Maced J Med Sci. 2019;7(16):2626\u20139.","journal-title":"Open Access Maced J Med Sci"},{"key":"1215_CR35","doi-asserted-by":"publisher","first-page":"e2016011","DOI":"10.4178\/epih.e2016011","volume":"38","author":"M Kazemi","year":"2016","unstructured":"Kazemi M, Moghimbeigi A, Kiani J, Mahjub H, Faradmal J. Diabetic peripheral neuropathy class prediction by multicategory support vector machine model: a cross-sectional study. Epidemiol Health. 2016;38:e2016011.","journal-title":"Epidemiol Health"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-01215-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-020-01215-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-01215-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T23:35:14Z","timestamp":1629761714000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-020-01215-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,24]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1215"],"URL":"https:\/\/doi.org\/10.1186\/s12911-020-01215-w","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,24]]},"assertion":[{"value":"10 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The local ethics committee of ITMO university approved the study.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"201"}}