A new data mining methodology applied to the modelling of the influence of diet and lifestyle on the value of bone mineral density in post-menopausal women

In this work, the application of ‘multivariate adaptive regression splines’ (MARS) for modelling osteoporosis is described. This article focuses on the explanation of a new technique that combines the use of the principal components analysis (PCA) method with MARS. The use of this new technique allows for an easier management of large databases with a lower computational cost as the PCA allows the elimination of those variables that are redundant from the point of view of the phenomena under study. Osteoporosis is characterized by low ‘bone mineral density’ (BMD). This illness has a high-cost impact in all developed countries. The aim of this article is the development of a mathematical method capable of predicting the BMD of post-menopausal women, taking into account only certain nutritional variables. A nutritional habits and lifestyle questionnaire is drawn up. The variables obtained from this, together with the BMD of the patients calculated by densitometry, are processed using the ‘principal componen...