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000095477 0247_ $$2doi$$a10.3390/app10175942
000095477 0248_ $$2sideral$$a119533
000095477 037__ $$aART-2020-119533
000095477 041__ $$aeng
000095477 100__ $$0(orcid)0000-0001-8737-5814$$ade la Torre, Juan
000095477 245__ $$aApplying machine learning for healthcare: A case study on cervical pain assessment with motion capture
000095477 260__ $$c2020
000095477 5060_ $$aAccess copy available to the general public$$fUnrestricted
000095477 5203_ $$aGiven the exponential availability of data in health centers and the massive sensorization that is expected, there is an increasing need to manage and analyze these data in an effective way. For this purpose, data mining (DM) and machine learning (ML) techniques would be helpful. However, due to the specific characteristics of the field of healthcare, a suitable DM and ML methodology adapted to these particularities is required. The applied methodology must structure the different stages needed for data-driven healthcare, from the acquisition of raw data to decision-making by clinicians, considering the specific requirements of this field. In this paper, we focus on a case study of cervical assessment, where the goal is to predict the potential presence of cervical pain in patients affected with whiplash diseases, which is important for example in insurance-related investigations. By analyzing in detail this case study in a real scenario, we show how taking care of those particularities enables the generation of reliable predictive models in the field of healthcare. Using a database of 302 samples, we have generated several predictive models, including logistic regression, support vector machines, k-nearest neighbors, gradient boosting, decision trees, random forest, and neural network algorithms. The results show that it is possible to reliably predict the presence of cervical pain (accuracy, precision, and recall above 90%). We expect that the procedure proposed to apply ML techniques in the field of healthcare will help technologists, researchers, and clinicians to create more objective systems that provide support to objectify the diagnosis, improve test treatment efficacy, and save resources.
000095477 536__ $$9info:eu-repo/grantAgreement/ES/AEI-FEDER/TIN2016-78011-C4-3-R$$9info:eu-repo/grantAgreement/ES/DGA/T64-20R
000095477 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000095477 590__ $$a2.679$$b2020
000095477 591__ $$aPHYSICS, APPLIED$$b73 / 160 = 0.456$$c2020$$dQ2$$eT2
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000095477 591__ $$aMATERIALS SCIENCE, MULTIDISCIPLINARY$$b201 / 333 = 0.604$$c2020$$dQ3$$eT2
000095477 592__ $$a0.435$$b2020
000095477 593__ $$aComputer Science Applications$$c2020$$dQ2
000095477 593__ $$aEngineering (miscellaneous)$$c2020$$dQ2
000095477 593__ $$aProcess Chemistry and Technology$$c2020$$dQ2
000095477 593__ $$aInstrumentation$$c2020$$dQ2
000095477 593__ $$aMaterials Science (miscellaneous)$$c2020$$dQ2
000095477 593__ $$aFluid Flow and Transfer Processes$$c2020$$dQ2
000095477 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000095477 700__ $$0(orcid)0000-0003-4527-3267$$aMarin, Javier$$uUniversidad de Zaragoza
000095477 700__ $$0(orcid)0000-0002-7073-219X$$aIlarri, Sergio$$uUniversidad de Zaragoza
000095477 700__ $$0(orcid)0000-0003-3223-1324$$aMarin, José J.$$uUniversidad de Zaragoza
000095477 7102_ $$15002$$2720$$aUniversidad de Zaragoza$$bDpto. Ingeniería Diseño Fabri.$$cÁrea Proyectos de Ingeniería
000095477 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000095477 773__ $$g10, 17 (2020), 5942 [28 pp.]$$pAppl. sci.$$tAPPLIED SCIENCES-BASEL$$x2076-3417
000095477 8564_ $$s2808251$$uhttps://zaguan.unizar.es/record/95477/files/texto_completo.pdf$$yVersión publicada
000095477 8564_ $$s515254$$uhttps://zaguan.unizar.es/record/95477/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
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000095477 951__ $$a2021-09-02-09:38:08
000095477 980__ $$aARTICLE