Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 367345, 9 pages
http://dx.doi.org/10.1155/2012/367345
Research Article
Analysis of a Multilevel Diagnosis Decision Support System and Its Implications: A Case Study
1Centre for Plant Biotechnology and Genomics UPM-INIA, Polytechnic University of Madrid, Parque Científico y Tecnológico de la U.P.M. Campus de Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain
2Computer Science Department, Universidad Carlos III de Madrid, Avenida Universidad 30, 28911 Leganés, Spain
3Research Programme on Biomedical Informatics (GRIB), IMIM-Universitat Pompeu Fabra, Dr. Aiguader, 88, 08003 Barcelona, Spain
4Division of Research and Postgraduate Studies, Instituto Tecnológico de Orizaba, Avenida Oriente 9 No. 852 Col. Emiliano Zapata, 94320 Orizaba, ER, Mexico
Received 26 July 2012; Revised 13 September 2012; Accepted 14 September 2012
Academic Editor: Edelmira Valero
Copyright © 2012 Alejandro Rodríguez-González et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Medical diagnosis can be performed in an automatic way with the use of computer-based systems or algorithms. Such systems are usually called diagnostic decision support systems (DDSSs) or medical diagnosis systems (MDSs). An evaluation of the performance of a DDSS called ML-DDSS has been performed in this paper. The methodology is based on clinical case resolution performed by physicians which is then used to evaluate the behavior of ML-DDSS. This methodology allows the calculation of values for several well-known metrics such as precision, recall, accuracy, specificity, and Matthews correlation coefficient (MCC). Analysis of the behavior of ML-DDSS reveals interesting results about the behavior of the system and of the physicians who took part in the evaluation process. Global results show how the ML-DDSS system would have significant utility if used in medical practice. The MCC metric reveals an improvement of about 30% in comparison with the experts, and with respect to sensitivity the system returns better results than the experts.