{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T14:56:48Z","timestamp":1777647408533,"version":"3.51.4"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T00:00:00Z","timestamp":1656115200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T00:00:00Z","timestamp":1656115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"All India Institue of Medical Sciences Raipur"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s11517-022-02617-w","type":"journal-article","created":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T15:02:54Z","timestamp":1656169374000},"page":"2349-2357","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Machine learning algorithm to evaluate risk factors of diabetic foot ulcers and its severity"],"prefix":"10.1007","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6808-6176","authenticated-orcid":false,"given":"Rachita","family":"Nanda","sequence":"first","affiliation":[]},{"given":"Abhigyan","family":"Nath","sequence":"additional","affiliation":[]},{"given":"Suprava","family":"Patel","sequence":"additional","affiliation":[]},{"given":"Eli","family":"Mohapatra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,25]]},"reference":[{"issue":"1","key":"2617_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12902-018-0270-2","volume":"18","author":"SP Vibha","year":"2018","unstructured":"Vibha SP, Kulkarni MM, Kirthinath Ballala AB, Kamath A, Maiya GA (2018) Community based study to assess the prevalence of diabetic foot syndrome and associated risk factors among people with diabetes mellitus. BMC Endocr Disord 18(1):1\u20139","journal-title":"BMC Endocr Disord"},{"key":"2617_CR2","doi-asserted-by":"publisher","unstructured":"Armstrong DG, Swerdlow MA, Armstrong AA, Conte MS, Padula WV, Bus SA (2020) Five year mortality and direct costs of care for people with diabetic foot complications are comparable to cancer. J Foot Ankle Res13(1):16. https:\/\/doi.org\/10.1186\/s13047-020-00383-2","DOI":"10.1186\/s13047-020-00383-2"},{"issue":"1","key":"2617_CR3","doi-asserted-by":"publisher","first-page":"e001207","DOI":"10.1136\/bmjdrc-2020-001207","volume":"8","author":"WB Aan de Stegge","year":"2020","unstructured":"Aan de Stegge WB, Abu-Hanna A, Bus SA (2020) Development of a multivariable prediction model for plantar foot ulcer recurrence in high-risk people with diabetes. BMJ Open Diabetes Res Care 8(1):e001207","journal-title":"BMJ Open Diabetes Res Care"},{"issue":"5","key":"2617_CR4","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1109\/TETCI.2018.2866254","volume":"4","author":"M Goyal","year":"2020","unstructured":"Goyal M, Reeves ND, Davison AK, Rajbhandari S, Spragg J, Yap MH (2020) DFUNet: convolutional neural networks for diabetic foot ulcer classification. IEEE Trans Emerg Top Comput Intell 4(5):728\u2013739","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"issue":"21","key":"2617_CR5","doi-asserted-by":"publisher","first-page":"15655","DOI":"10.1007\/s11042-019-07820-w","volume":"79","author":"L Alzubaidi","year":"2020","unstructured":"Alzubaidi L, Fadhel MA, Oleiwi SR, Al-Shamma O, Zhang J (2020) DFU_QUTNet: diabetic foot ulcer classification using novel deep convolutional neural network. Multimed Tools Appl [Internet]. 79(21):15655\u201377. https:\/\/doi.org\/10.1007\/s11042-019-07820-w","journal-title":"Multimed Tools Appl [Internet]."},{"key":"2617_CR6","doi-asserted-by":"publisher","first-page":"198977","DOI":"10.1109\/ACCESS.2020.3035327","volume":"8","author":"J Tulloch","year":"2020","unstructured":"Tulloch J, Zamani R, Akrami M (2020) Machine learning in the prevention, diagnosis and management of diabetic foot ulcers: a systematic review. IEEE Access 8:198977\u2013199000","journal-title":"IEEE Access"},{"issue":"10","key":"2617_CR7","doi-asserted-by":"publisher","first-page":"2285","DOI":"10.2337\/dc10-1303","volume":"33","author":"S Tesfaye","year":"2010","unstructured":"Tesfaye S, Boulton AJM, Dyck PJ, Freeman R, Horowitz M, Kempler P et al (2010) Diabetic neuropathies: update on definitions, diagnostic criteria, estimation of severity, and treatments. Diabetes Care 33(10):2285\u20132293","journal-title":"Diabetes Care"},{"key":"2617_CR8","unstructured":"Frykberg RG (2002) Diabetic foot ulcers: pathogenesis and management. Am Fam Physician 66(9):1655\u20131662"},{"issue":"1","key":"2617_CR9","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random Forests. Mach Learn [Internet]. 45(1):5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn [Internet]."},{"key":"2617_CR10","doi-asserted-by":"crossref","unstructured":"Nath A, Subbiah K (2015) Maximizing lipocalin prediction through balanced and diversified training set and decision fusion. Comput Biol Chem [Internet]. 59:101\u201310. Available from: http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1476927115301821","DOI":"10.1016\/j.compbiolchem.2015.09.011"},{"issue":"2","key":"2617_CR11","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1023\/A:1018054314350","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging Predictors. Mach Learn [Internet]. 24(2):123\u201340. https:\/\/doi.org\/10.1023\/A:1018054314350","journal-title":"Mach Learn [Internet]."},{"issue":"4","key":"2617_CR12","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/5254.708428","volume":"13","author":"MA Hearst","year":"1998","unstructured":"Hearst MA (1998) 1998 Support vector machines. IEEE Intell Syst [Internet]. 13(4):18\u201328. https:\/\/doi.org\/10.1109\/5254.708428","journal-title":"IEEE Intell Syst [Internet]."},{"issue":"3","key":"2617_CR13","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1162\/089976601300014493","volume":"13","author":"SS Keerthi","year":"2001","unstructured":"Keerthi SS, Shevade SK, Bhattacharyya C, Murthy KRK (2001) Improvements to Platt\u2019s SMO algorithm for SVM classifier design. Neural Comput 13(3):637\u2013649","journal-title":"Neural Comput"},{"key":"2617_CR14","doi-asserted-by":"publisher","unstructured":"Nath A, Subbiah K (2016) Insights into the molecular basis of piezophilic adaptation: extraction of piezophilic signatures. J Theor Biol 7:390:117\u2013126. https:\/\/doi.org\/10.1016\/j.jtbi.2015.11.021","DOI":"10.1016\/j.jtbi.2015.11.021"},{"key":"2617_CR15","doi-asserted-by":"publisher","unstructured":"Kramer O (2013) K-Nearest Neighbors. In: Dimensionality reduction with unsupervised nearest neighbors Intelligent Systems Reference Library, Vol 51. Springer Berlin Heidelberg. https:\/\/doi.org\/10.1007\/978-3-642-38652-7_2","DOI":"10.1007\/978-3-642-38652-7_2"},{"key":"2617_CR16","doi-asserted-by":"publisher","unstructured":"Webb GI (2011) Na\u00efve Bayes. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Boston, MA. https:\/\/doi.org\/10.1007\/978-0-387-30164-8_576","DOI":"10.1007\/978-0-387-30164-8_576"},{"issue":"3","key":"2617_CR17","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","volume":"6","author":"R Polikar","year":"2006","unstructured":"Polikar R (2006) Ensemble based systems in decision making. IEEE Circuits Syst Mag 6(3):21\u201345","journal-title":"IEEE Circuits Syst Mag"},{"key":"2617_CR18","unstructured":"Seewald AK (2002) How to make stacking better and faster while also taking care of an unknown weakness. Proceedings of the Nineteenth International Conference on Machine Learning. Morgan Kaufmann Publishers Inc., p 554\u2013561"},{"key":"2617_CR19","unstructured":"Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. Proceedings of the Thirteenth International Conference on International Conference on Machine Learning. Morgan Kaufmann Publishers Inc., Bari, Italy, p 148\u2013156"},{"key":"2617_CR20","doi-asserted-by":"crossref","unstructured":"Wei Q, Dunbrack Jr RL (2013) The role of balanced training and testing data sets for binary classifiers in bioinformatics. PLoS One 8(7):e67863\u2013e67863. https:\/\/pubmed.ncbi.nlm.nih.gov\/23874456","DOI":"10.1371\/journal.pone.0067863"},{"key":"2617_CR21","doi-asserted-by":"crossref","unstructured":"Nath A, Subbiah K (2018) The role of pertinently diversified and balanced training as well as testing data sets in achieving the true performance of classifiers in predicting the antifreeze proteins. Neurocomputing 272:294\u2013305. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231217312341","DOI":"10.1016\/j.neucom.2017.07.004"},{"issue":"1","key":"2617_CR22","first-page":"321","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Int Res 16(1):321\u2013357","journal-title":"J Artif Int Res"},{"key":"2617_CR23","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez-Maro\u00f1o N, Alonso-Betanzos A, Tombilla-Sanrom\u00e1n M (2007) Filter methods for feature selection: a comparative study. Proceedings of the 8th international conference on Intelligent data engineering and automated learning. Springer-Verlag, Birmingham, UK, p 178\u2013187","DOI":"10.1007\/978-3-540-77226-2_19"},{"key":"2617_CR24","doi-asserted-by":"publisher","unstructured":"Nath A (2016) Insights into the sequence parameters for halophilic adaptation. Amino Acids [Internet]. 48(3):751\u201362. Available from: https:\/\/doi.org\/10.1007\/s00726-015-2123-x","DOI":"10.1007\/s00726-015-2123-x"},{"key":"2617_CR25","doi-asserted-by":"crossref","unstructured":"Urbanowicz RJ, Meeker M, La Cava W, Olson RS, Moore JH (2018) Relief-based feature selection: Introduction and review. J Biomed Inform [Internet]. 85:189\u2013203. Available from: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1532046418301400","DOI":"10.1016\/j.jbi.2018.07.014"},{"key":"2617_CR26","doi-asserted-by":"publisher","unstructured":"Roobaert D, Karakoulas G, Chawla NV (2006) Information gain, correlation and support vector machines. In: Guyon I, Nikravesh M, Gunn S, Zadeh LA (eds) Feature extraction. Studies in fuzziness and soft computing, vol 207. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-540-35488-8_23","DOI":"10.1007\/978-3-540-35488-8_23"},{"key":"2617_CR27","unstructured":"Karegowda A, Manjunath A, Jayram MA (2010) Comparative study of attribute selection using gain ratio and correlation based feature selection. Int J Inf Technol Knowl Manag 2(2):271\u2013277"},{"key":"2617_CR28","unstructured":"Liu H, Setiono R (1995) Chi2: feature selection and discretization of numeric attributes. In: Anon (ed) Proceedings of the International Conference on Tools with Artificial Intelligence. IEEE, pp 388\u2013391"},{"issue":"1","key":"2617_CR29","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl [Internet]. 11(1):10\u201318. https:\/\/doi.org\/10.1145\/1656274.1656278","journal-title":"SIGKDD Explor Newsl [Internet]."},{"issue":"1","key":"2617_CR30","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s12864-019-6413-7","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics [Internet]. 21(1):6. https:\/\/doi.org\/10.1186\/s12864-019-6413-7","journal-title":"BMC Genomics [Internet]."},{"key":"2617_CR31","doi-asserted-by":"publisher","unstructured":"Zhang E, Zhang Y.F-Measure. In: Liu L, \u00d6Zsu MT (eds) Encyclopedia of database systems. Springer, NewYork, NY. https:\/\/doi.org\/10.1007\/978-1-4614-8265-9_483","DOI":"10.1007\/978-1-4614-8265-9_483"},{"issue":"7","key":"2617_CR32","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","volume":"30","author":"AP Bradley","year":"1997","unstructured":"Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn [Internet]. 30(7):1145\u20131159. https:\/\/doi.org\/10.1016\/S0031-3203(96)00142-2","journal-title":"Pattern Recogn [Internet]."},{"issue":"7","key":"2617_CR33","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1162\/neco.1996.8.7.1341","volume":"8","author":"DH Wolpert","year":"1996","unstructured":"Wolpert DH (1996) The lack of a priori distinctions between learning algorithms. Neural Comput 8(7):1341\u20131390","journal-title":"Neural Comput"},{"key":"2617_CR34","doi-asserted-by":"publisher","unstructured":"Steen EH, Wang X, Balaji S, Butte MJ, Bollyky PL, Keswani SG (2020) The role of the anti-inflammatory cytokine interleukin-10 in tissue fibrosis. Adv Wound Care (New Rochelle) 9(4):184\u2013198. https:\/\/doi.org\/10.1089\/wound.2019.1032","DOI":"10.1089\/wound.2019.1032"},{"issue":"6","key":"2617_CR35","doi-asserted-by":"publisher","first-page":"525","DOI":"10.2217\/dmt.15.44","volume":"5","author":"CE DeClue","year":"2015","unstructured":"DeClue CE, Shornick LP (2015) The cytokine milieu of diabetic wounds. Diabetes Manag 5(6):525\u2013535","journal-title":"Diabetes Manag"},{"issue":"6","key":"2617_CR36","doi-asserted-by":"publisher","first-page":"21244","DOI":"10.1038\/srep21244","volume":"17","author":"JC Barry","year":"2016","unstructured":"Barry JC, Shakibakho S, Durrer C, Simtchouk S, Jawanda KK, Cheung ST et al (2016) Hyporesponsiveness to the anti-inflammatory action of interleukin-10 in type 2 diabetes. Sci Rep 17(6):21244. https:\/\/doi.org\/10.1038\/srep21244","journal-title":"Sci Rep"},{"issue":"3","key":"2617_CR37","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/s12291-015-0513-1","volume":"31","author":"M Mangaraj","year":"2016","unstructured":"Mangaraj M, Nanda R, Panda S (2016) Apolipoprotein A-I: a molecule of diverse function. Indian J Clin Biochem 31(3):253\u2013259. https:\/\/doi.org\/10.1007\/s12291-015-0513-1","journal-title":"Indian J Clin Biochem"},{"issue":"10","key":"2617_CR38","doi-asserted-by":"publisher","first-page":"4676","DOI":"10.4049\/jimmunol.1402870","volume":"194","author":"I Tiniakou","year":"2015","unstructured":"Tiniakou I, Drakos E, Sinatkas V, Van Eck M, Zannis VI, Boumpas D et al (2015) High-density lipoprotein attenuates Th1 and th17 autoimmune responses by modulating dendritic cell maturation and function. J Immunol 194(10):4676\u20134687. https:\/\/doi.org\/10.4049\/jimmunol.1402870","journal-title":"J Immunol"},{"issue":"4","key":"2617_CR39","first-page":"507","volume":"59","author":"B Bruhn-Olszewska","year":"2012","unstructured":"Bruhn-Olszewska B, Koron-Burakowska A, Gabig-Ciminska M, Olszweski P, Wegrzyn A, Jakobkiewicz-Banecka J (2012) Molecular factors involved in the development of diabetic foot syndrome. Acta Biochimica Plonica 59(4):507\u2013513","journal-title":"Acta Biochimica Plonica"},{"key":"2617_CR40","doi-asserted-by":"publisher","first-page":"17056","DOI":"10.1038\/s41598-020-74071-3","volume":"10","author":"Xf Xiong","year":"2020","unstructured":"Xiong Xf, Wei L, Xiao Y, An YC, Yang J, Zhao H et al (2020) Family history of diabetes is associated with diabetic foot complications in type 2 diabetes. Sci Rep 10:17056. https:\/\/doi.org\/10.1038\/s41598-020-74071-3","journal-title":"Sci Rep"},{"issue":"11","key":"2617_CR41","doi-asserted-by":"publisher","first-page":"2132","DOI":"10.1097\/HJH.0000000000001815","volume":"36","author":"P Palatini","year":"2018","unstructured":"Palatini P (2018) Risk of developing foot ulcers in diabetes. J of Hypertens 36(11):2132\u20132134. https:\/\/doi.org\/10.1097\/HJH.0000000000001815","journal-title":"J of Hypertens"},{"issue":"6","key":"2617_CR42","doi-asserted-by":"publisher","first-page":"787","DOI":"10.4239\/wjd.v5.i6.787","volume":"5","author":"A Kushiyama","year":"2014","unstructured":"Kushiyama A, Tanaka K, Hara S, Kawazu S (2014) Linking uric acid metabolism to diabetic complications. World J Diabetes 5(6):787\u2013795. https:\/\/doi.org\/10.4239\/wjd.v5.i6.787","journal-title":"World J Diabetes"},{"key":"2617_CR43","doi-asserted-by":"publisher","first-page":"9691345","DOI":"10.1155\/2019\/9691345","volume":"2019","author":"Q Xiong","year":"2019","unstructured":"Xiong Q, Liu J, Xu Y (2019) Effects of uric acid on diabetes mellitus and its chronic complications. Int J Endocrinol 2019:9691345. https:\/\/doi.org\/10.1155\/2019\/9691345","journal-title":"Int J Endocrinol"},{"key":"2617_CR44","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.2147\/DMSO.S256243","volume":"13","author":"M Jalilian","year":"2020","unstructured":"Jalilian M, AhmadiSarbarzeh P, Oubari S (2020) Factors related to severity of diabetic foot ulcer: a systematic review. Diabetes MetabSyndrObes 13:1835\u20131842. https:\/\/doi.org\/10.2147\/DMSO.S256243","journal-title":"Diabetes MetabSyndrObes"},{"key":"2617_CR45","doi-asserted-by":"publisher","unstructured":"Parisi MCR, Moura Neto A, Menezes FH, Gomes MB, Teixeria RM, de Oliveira JEP et al (2016) Baseline characteristics and risk factors for ulcer, amputation and severe neuropathy in diabetic foot at risk: the BRAZUPA study. Diabetol Metab Syndr 8:25. https:\/\/doi.org\/10.1186\/s13098-016-0126-8","DOI":"10.1186\/s13098-016-0126-8"},{"key":"2617_CR46","doi-asserted-by":"publisher","first-page":"e034058","DOI":"10.1136\/bmjopen-2019-034058","volume":"10","author":"PC Banik","year":"2020","unstructured":"Banik PC, Barua L, Moniruzzaman M, Mondal R, Zaman F, Ali L (2020) Risk of diabetic foot ulcer and its associated factors among Bangladeshi subjects:a multicentric cross-sectional study. BMJ Open 10:e034058. https:\/\/doi.org\/10.1136\/bmjopen-2019-034058","journal-title":"BMJ Open"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02617-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02617-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02617-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T03:44:44Z","timestamp":1658202284000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02617-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,25]]},"references-count":46,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["2617"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02617-w","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,25]]},"assertion":[{"value":"11 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethical clearance obtained vide IEC proposal no. AIIMSRPR\/IEC\/2018\/128.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}