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Visual Data Mining in Physiotherapy Using Self-Organizing Maps: A New Approximation to the Data Analysis
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Author(s): Yasser Alakhdar (University of Valencia, Spain), José M. Martínez-Martínez (University of Valencia, Spain), Josep Guimerà-Tomás (University of Valencia, Spain), Pablo Escandell-Montero (University of Valencia, Spain), Josep Benitez (University of Valencia, Spain)and Emilio Soria-Olivas (University of Valencia, Spain)
Copyright: 2012
Pages: 8
Source title:
Medical Applications of Intelligent Data Analysis: Research Advancements
Source Author(s)/Editor(s): Rafael Magdalena-Benedito (Intelligent Data Analysis Laboratory, University of Valencia, Spain), Emilio Soria-Olivas (Intelligent Data Analysis Laboratory, University of Valencia, Spain), Juan Guerrero Martínez (Intelligent Data Analysis Laboratory, University of Valencia, Spain), Juan Gómez-Sanchis (Intelligent Data Analysis Laboratory, University of Valencia, Spain)and Antonio Jose Serrano-López (Intelligent Data Analysis Laboratory, University of Valencia, Spain)
DOI: 10.4018/978-1-4666-1803-9.ch012
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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.
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