{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T06:57:06Z","timestamp":1722409026126},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,4,17]],"date-time":"2019-04-17T00:00:00Z","timestamp":1555459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,4,17]],"date-time":"2019-04-17T00:00:00Z","timestamp":1555459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The aim of the study was to investigate how to improve the forecasting of craniofacial unbalance risk during growth among patients affected by Class III malocclusion. To this purpose we used computational methodologies such as Transductive Learning (TL), Boosting (B), and Feature Engineering (FE) instead of the traditional statistical analysis based on Classification trees and logistic models. Such techniques have been applied to cephalometric data from 728 cross-sectional untreated Class III subjects\u00a0(6\u201314 years of age) and from 91 untreated Class III subjects followed longitudinally during the growth process. A cephalometric analysis comprising 11 variables has also been performed. The subjects followed longitudinally were divided into two subgroups: favourable and unfavourable growth, in comparison with normal craniofacial growth. With respect to traditional statistical predictive analytics, TL increased the accuracy in identifying subjects at risk of unfavourable growth. TL algorithm was useful in diffusion of information from longitudinal to cross-sectional subjects. The accuracy in identifying high-risk subjects to growth worsening increased from 63% to 78%. Finally, a further increase in identification accuracy, up to 83%, was produced by FE. A ranking of important variables in identifying subjects at risk of growth worsening, therefore,\u00a0has been obtained.<\/jats:p>","DOI":"10.1038\/s41598-019-42384-7","type":"journal-article","created":{"date-parts":[[2019,4,17]],"date-time":"2019-04-17T10:14:19Z","timestamp":1555496059000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Exploiting the interplay between cross-sectional and longitudinal data in Class III malocclusion patients"],"prefix":"10.1038","volume":"9","author":[{"given":"Enrico","family":"Barelli","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ennio","family":"Ottaviani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pietro","family":"Auconi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guido","family":"Caldarelli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Veronica","family":"Giuntini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"suffix":"Jr.","given":"James A.","family":"McNamara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lorenzo","family":"Franchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,4,17]]},"reference":[{"key":"42384_CR1","doi-asserted-by":"crossref","unstructured":"Sullivan, R. Introduction to Data Mining for Life Science (Springer, 2012).","DOI":"10.1007\/978-1-59745-290-8"},{"key":"42384_CR2","unstructured":"Russel, S. J. & Norvig, P. Artificial Intelligence: A Modern Approach (Prentice Hall, 2009)."},{"key":"42384_CR3","doi-asserted-by":"crossref","unstructured":"Finlay, S. Predictive Analytics, Data Mining, Big Data (Palgrave, 2014).","DOI":"10.1057\/9781137379283"},{"issue":"1","key":"42384_CR4","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/S0933-3657(01)00077-X","volume":"23","author":"Igor Kononenko","year":"2001","unstructured":"Kononenko, I. Machine Learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in Medicine 23 (2001).","journal-title":"Artificial Intelligence in Medicine"},{"issue":"1","key":"42384_CR5","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1146\/annurev.bioeng.8.061505.095802","volume":"8","author":"Paul Sajda","year":"2006","unstructured":"Sajda, P. Machine learning for detection and diagnosis of disease. Annual review of Biomedical Engineering 8 (2006).","journal-title":"Annual Review of Biomedical Engineering"},{"key":"42384_CR6","doi-asserted-by":"crossref","unstructured":"Bellazzi, R. & Zupan, B. Predictive Data Mining in clinical medicine: current issues and guidelines. International Journal of Medical Informatics 77 (2008).","DOI":"10.1016\/j.ijmedinf.2006.11.006"},{"key":"42384_CR7","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/nrg2918","volume":"12","author":"AL Barab\u00e1si","year":"2011","unstructured":"Barab\u00e1si, A. L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nature Reviews Genetics 12, 56\u201368 (2011).","journal-title":"Nature Reviews Genetics"},{"key":"42384_CR8","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.ins.2016.04.040","volume":"361","author":"G Forestier","year":"2016","unstructured":"Forestier, G. & Wemmert, G. Semi-supervised learning using multiple clustering with limited clustering data. Information Sciences 361, 48\u201365 (2016).","journal-title":"Information Sciences"},{"key":"42384_CR9","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1053\/j.sodo.2007.05.006","volume":"13","author":"T Baccetti","year":"2007","unstructured":"Baccetti, T., Franchi, L. & McNamara, J. A. Jr. Growth in the untreated Class III subjects. Seminars in Orthodontics 13, 130\u2013142 (2007).","journal-title":"Seminars in Orthodontics"},{"key":"42384_CR10","first-page":"1886","volume":"9","author":"A Kauser","year":"2017","unstructured":"Kauser, A. Analysis of data mining tools for disease prediction. Journal of Pharmaceutical Science and Research 9, 1886\u20131888 (2017).","journal-title":"Journal of Pharmaceutical Science and Research"},{"key":"42384_CR11","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1145\/2347736.2347755","volume":"55","author":"P Domingos","year":"2012","unstructured":"Domingos, P. A few useful things to know about machine learning. Communications of the ACM 55, 78\u201387 (2012).","journal-title":"Communications of the ACM"},{"key":"42384_CR12","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9780262033589.001.0001","volume-title":"The semi-supervised learning book","author":"O Chapelle","year":"2006","unstructured":"Chapelle, O., Scholkopf, B. & Zien, A. The semi-supervised learning book. (MIT Press, Cambridge, MA, 2006)."},{"key":"42384_CR13","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1023\/A:1009752403260","volume":"1","author":"S Salzberg","year":"1997","unstructured":"Salzberg, S. On comparing classifiers: pitfall to avoid and a recommended approach. Data Mining and Knowledge Discovery 1, 317\u2013327 (1997).","journal-title":"Data Mining and Knowledge Discovery"},{"key":"42384_CR14","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund, Y. & Schapire, R. A decision-theoretic generalization of on-line learning and an application to Boosting. Journal of Computer and System Sciences 55, 3\u201343 (1997).","journal-title":"Journal of Computer and System Sciences"},{"key":"42384_CR15","unstructured":"Zhu, X. Semi-supervised learning literature survey. typeTech. Rep. TR 1530 (Computer Sciences, Univeristy of Wisconsin, Madison, 2007)."},{"key":"42384_CR16","doi-asserted-by":"publisher","first-page":"cjw084","DOI":"10.1093\/ejo\/cjw084","volume":"39","author":"P Auconi","year":"2017","unstructured":"Auconi, P. et al. Understanding interactions among cephalometrics variables during growth in untreated Class III subjects. The European Journal of Orthodontics 39, cjw084 (2017).","journal-title":"The European Journal of Orthodontics"},{"key":"42384_CR17","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1093\/ejo\/cjt036","volume":"89","author":"P Auconi","year":"2014","unstructured":"Auconi, P., Scazzocchio, M., Defraia, E., McNamara, J. A. Jr. & Franchi, L. Forecasting craniofacial growth in individuals with Class III malocclusion by computational modeling. European Journal of Orthodontics 89, 207\u2013213 (2014).","journal-title":"European Journal of Orthodontics"},{"key":"42384_CR18","doi-asserted-by":"publisher","first-page":"e44521","DOI":"10.1371\/journal.pone.0044521","volume":"7","author":"A Scala","year":"2012","unstructured":"Scala, A. et al. Using networks to understand medical data: the case of Class III malocclusions. PloS one 7, e44521 (2012).","journal-title":"PloS one"},{"key":"42384_CR19","first-page":"577","volume":"76","author":"B Reyes","year":"2006","unstructured":"Reyes, B., Baccetti, T. & McNamara, J. A. Jr. An estimate of craniofacial growth in Class III malocclusion. The Angle Orthodontist 76, 577\u2013584 (2006).","journal-title":"The Angle Orthodontist"},{"key":"42384_CR20","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1016\/S0002-9416(84)90352-X","volume":"86","author":"JAJ McNamara","year":"1984","unstructured":"McNamara, J. A. Jr. A method of cephalometric evaluation. American Journal of Orthodontics 86, 449\u2013470 (1984).","journal-title":"American Journal of Orthodontics"},{"key":"42384_CR21","volume-title":"A Manual of Facial Growth","author":"SN Bathia","year":"1993","unstructured":"Bathia, S. N. & Leighton, B. C. A Manual of Facial Growth. (Oxford University Press, Oxford (UK), 1993)."},{"key":"42384_CR22","doi-asserted-by":"crossref","unstructured":"Hastie, Trevor & Tibshirani. The Elements of Statistical Learning (Springer-Verlag, 2009).","DOI":"10.1007\/978-0-387-84858-7"},{"key":"42384_CR23","doi-asserted-by":"crossref","unstructured":"Hastie T., Tibshirani R. & Wainwright M. Statistical Learning with Sparsity (Springer\u2013Verlag, 2015).","DOI":"10.1201\/b18401"},{"key":"42384_CR24","doi-asserted-by":"crossref","unstructured":"Caldarelli, G. Scale-Free Networks (Oxford University Press, 2007).","DOI":"10.1093\/acprof:oso\/9780199211517.001.0001"},{"key":"42384_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4236\/jilsa.2017.91001","volume":"9","author":"M Fatima","year":"2017","unstructured":"Fatima, M. & Pasha, M. Survey of machine Learning algorithms for disease diagnostic. Journal of Intelligent Learning Systems and Applications 9, 1\u201316 (2017).","journal-title":"Journal of Intelligent Learning Systems and Applications"},{"key":"42384_CR26","unstructured":"Chen, T. & Guestrin, C. A scalable Tree Bosting System. In 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785\u2013794 (ACM, 2016)."},{"key":"42384_CR27","first-page":"197","volume":"5","author":"RE Schapire","year":"1990","unstructured":"Schapire, R. E. The strength of weak learnability. Machine Learning 5, 197\u2013227 (1990).","journal-title":"Machine Learning"},{"key":"42384_CR28","unstructured":"Chawla, N. V. Data Mining for imbalanced dataset: an overview. In Maimon, O. & Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook (Springer, 2005)."},{"key":"42384_CR29","first-page":"64","volume":"4","author":"M Henrion","year":"1991","unstructured":"Henrion, M., Breese, J. S. & Horvitz, E. J. Decision analysis and expert systems. Artificial Intelligence Magazine 4, 64\u201391 (1991).","journal-title":"Artificial Intelligence Magazine"},{"key":"42384_CR30","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. Random Forest. Machine Learning 45, 5\u201332 (2001).","journal-title":"Machine Learning"},{"key":"42384_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0169-2607(89)90117-X","volume":"30","author":"P Pis","year":"1989","unstructured":"Pis, P. & Mesiar, R. Fuzzy model of inexact reasoning in medicine. Computer methods and Programs in Biomedicine 30, 1\u20138 (1989).","journal-title":"Computer methods and Programs in Biomedicine"},{"key":"42384_CR32","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1109\/69.494161","volume":"8","author":"W Buntine","year":"1996","unstructured":"Buntine, W. A guide to the literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering 8, 195\u2013210 (1996).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"42384_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-15293-w","volume":"7","author":"M Scutari","year":"2017","unstructured":"Scutari, M., Auconi, P., Caldarelli, G. & Franchi, L. Bayesian Networks Analysis of Malocclusion Data. Scientific Reports 7, 15236 (2017).","journal-title":"Scientific Reports"},{"key":"42384_CR34","unstructured":"Vig, P. Reflection on the rationality of orthodontics. In: Science and Clinical Judgment in Orthodontics, Vig, K. & Ribbens, K. A. (eds) 9, 15\u201361 (1986)."},{"key":"42384_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1214\/088342306000000060","volume":"21","author":"D Hand","year":"2006","unstructured":"Hand, D. Classifier technology and the illusion of progress. Statistical Science 21, 1\u201314 (2006).","journal-title":"Statistical Science"},{"key":"42384_CR36","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1016\/j.ajodo.2006.02.025","volume":"129","author":"H Noroozi","year":"2006","unstructured":"Noroozi, H. Orthodontic treatment planning software. American Journal of Orthodontic and dentofacial Orthopedics 129, 834\u2013837 (2006).","journal-title":"American Journal of Orthodontic and dentofacial Orthopedics"},{"key":"42384_CR37","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/S0167-9473(01)00065-2","volume":"38","author":"J Friedman","year":"2002","unstructured":"Friedman, J. Stochastic gradient boosting. Computational Statistics and Data Analysis 38, 278\u2013367 (2002).","journal-title":"Computational Statistics and Data Analysis"},{"key":"42384_CR38","first-page":"37","volume":"2","author":"D Powers","year":"2011","unstructured":"Powers, D. Evaluation: From Precision Recall and F-Measure to ROC Informedness, Markedness & Correlation. Journal of Machine Learning Technologies 2, 37\u201363 (2011).","journal-title":"Journal of Machine Learning Technologies"},{"key":"42384_CR39","volume-title":"Scienceblind. Why our intuitive theories about the world are so often wrong","author":"A Shtulman","year":"2017","unstructured":"Shtulman, A. Scienceblind. Why our intuitive theories about the world are so often wrong. (Basic Books, NY, 2017)."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-019-42384-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-019-42384-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-019-42384-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T08:50:15Z","timestamp":1671267015000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-019-42384-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,17]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["42384"],"URL":"https:\/\/doi.org\/10.1038\/s41598-019-42384-7","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,17]]},"assertion":[{"value":"14 August 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 December 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"6189"}}