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Mario Aldape-Pérez, Cornelio Yáñez-Márquez, Oscar Camacho-Nieto, Itzamá López-Yáñez, Amadeo-José Argüelles-Cruz
Computers in Human Behavior, Volume 51, Part B, October 2015, Pages 771-779, ISSN 0747-5632,
Publication year: 2015


This paper addresses social networking and collaborative learning in the medical domain by focusing on two main objectives: the first one concerns about social networking between computer science experts and postgraduate students, while the second concerns about collaborative learning between medical experts and less experienced physicians. The tasks of algorithms testing and performance evaluation were assigned to computer science postgraduate students. They made extensive use of social networking in order to implement associative models to perform pattern classification tasks in medical datasets and share performance results. Associative memories have a number of properties, including a rapid, compute efficient best-match and intrinsic noise tolerance that make them ideal for diagnostic hypothesis-generation processes in the medical domain. Using supervised machine learning algorithms allows less experienced physicians to compare their diagnostic results between workgroups and verify whether their knowledge is consistent with the results delivered by computational tools. Throughout the experimental phase the proposed algorithm is applied to help diagnose diseases; particularly, it is applied in the diagnosis of five different problems in the medical field. The performance of the proposed model is validated by comparing classification accuracy of DAM against the performance achieved by other twenty well known algorithms. Experimental results have shown that DAM achieved the best performance in three of the five pattern classification problems in the medical field. Similarly, it should be noted that our proposal achieved the best classification accuracy averaged over all datasets. Experimental results confirm that the proposed algorithm can be a valuable tool for promoting collaborative learning among less experienced physicians.


  • Associative models;
  • Collaborative learning;
  • Medical datasets;
  • Pattern classification;
  • Social networking

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