Machine learning techniques applied to the determination of osteoporosis incidence in post-menopausal women

Authors:
C. OrdóñEz;J. M. MatíAs;J. F. De Cos Juez;P. J. GarcíA
Affiliations:
Department of Natural Resources and Environmental Engineering, Vigo University, E.T.S.I. MINAS, 36310 Vigo, Spain;Department of Statistics, Vigo University, E.T.S.I. MINAS, 36310 Vigo, Spain;Department of Mining Exploitation and Prospection, Oviedo University, 33004 Oviedo, Spain;Department of Applied Mathematics, Oviedo University, 33004 Oviedo, Spain
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2009

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Abstract

Osteoporosis is a disease that mostly affects women in developed countries. It is characterised by reduced bone mineral density (BMD) and results in a higher incidence of fractured or broken bones. In this research we studied the relationship between BMD and diet and lifestyle habits for a sample of 305 post-menopausal women by constructing a non-linear model using the regression support vector machines technique. One aim of this model was to make an initial preliminary estimate of BMD in the studied women (on the basis of a questionnaire with questions mostly on dietary habits) so as to determine whether they needed densitometry testing. A second aim was to determine the factors with the greatest bearing on BMD with a view to proposing dietary and lifestyle improvements. These factors were determined using regression trees applied to the support vector machines predictions.