Applying machine learning for healthcare: A case study on cervical pain assessment with motion capture

de la Torre, Juan ; Marin, Javier (Universidad de Zaragoza) ; Ilarri, Sergio (Universidad de Zaragoza) ; Marin, José J. (Universidad de Zaragoza)

Resumen: Given 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.

Idioma: Inglés

DOI: 10.3390/app10175942

Año: 2020

Publicado en: APPLIED SCIENCES-BASEL 10, 17 (2020), 5942 [28 pp.]
ISSN: 2076-3417

Factor impacto JCR: 2.679 (2020)

Categ. JCR: PHYSICS, APPLIED rank: 73 / 160 = 0.456 (2020) - Q2 - T2
Categ. JCR: ENGINEERING, MULTIDISCIPLINARY rank: 38 / 91 = 0.418 (2020) - Q2 - T2
Categ. JCR: CHEMISTRY, MULTIDISCIPLINARY rank: 101 / 178 = 0.567 (2020) - Q3 - T2
Categ. JCR: MATERIALS SCIENCE, MULTIDISCIPLINARY rank: 201 / 333 = 0.604 (2020) - Q3 - T2

Factor impacto SCIMAGO: 0.435 - Computer Science Applications (Q2) - Engineering (miscellaneous) (Q2) - Process Chemistry and Technology (Q2) - Instrumentation (Q2) - Materials Science (miscellaneous) (Q2) - Fluid Flow and Transfer Processes (Q2)

Financiación: info:eu-repo/grantAgreement/ES/AEI-FEDER/TIN2016-78011-C4-3-R
Financiación: info:eu-repo/grantAgreement/ES/DGA/T64-20R

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Proyectos de Ingeniería (Dpto. Ingeniería Diseño Fabri.)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)