Visual Data Mining in Physiotherapy Using Self-Organizing Maps

Author:

Alakhdar Yasser1,Martínez-Martínez José M.1,Guimerà-Tomás Josep1,Escandell-Montero Pablo1,Benitez Josep1,Soria-Olivas Emilio1

Affiliation:

1. University of Valencia, Spain

Abstract

The basis of all clinical science developments is the analysis of the data obtained from a particular problem. In recent decades, however, the capacity of computers to process data has been increasing exponentially, which has created the possibility of applying more powerful methods of data analysis. Among these methods, the multidimensional visual data mining methods are outstanding. These methods show all the variables of one particular problem on the whole allowing to the clinical specialist to extract his own conclusions. In this chapter, a neural approximation to this kind of data mining is shown by means of the valuation analysis of the knee in athletes in the pre- and post-surgery of the anterior cruciate ligament, studying variables of force and measurements at different distances of the knee.

Publisher

IGI Global

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. VSOM: Efficient, Stochastic Self-organizing Map Training;Advances in Intelligent Systems and Computing;2018-11-08

2. Self-Organizing Map Convergence;International Journal of Service Science, Management, Engineering, and Technology;2018-04

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