期刊
COMPTES RENDUS MECANIQUE
卷 347, 期 11, 页码 856-862出版社
ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
DOI: 10.1016/j.crme.2019.11.010
关键词
Machine Learning; Fracture risk; Osteoporosis; Locally Linear Embedding; Hyper-parametrization; Optimization
类别
资金
- Spanish Ministerio de Economia y Competitividad [DPI2017-89816-R]
- Spanish Generalitat Valenciana [Prometeo 2016/007]
- Spanish Ministerio de Educacion [FPU016/07122]
The hip fracture is one of the most common diseases for elder people and also, one of the most worrying one since it usually is the starting point of further complications for both, the health of the patient and their daily life. Additionally, reports shown that there exist differences between people living in different regions, thus limiting the use of global models. In this work we propose a hip fracture prediction tool for a local region, using clinical data of the population of that region. The data is processed with a dimensionality reduction tool in combination with and hyper-parametrization process and the corresponding hyper-parameter optimization process for obtaining good predictions in the diagnoses, as the results shown. (C) 2019 Academie des sciences. Published by Elsevier Masson SAS. All rights reserved.
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