Case based reasoning applied to medical diagnosis using multi-class classifier: A preliminary study

Authors

  • D. Viveros-Melo Universidad de Nariño
  • M. Ortega-Adarme Universidad de Nariño
  • X. Blanco Valencia Universidad de Salamanca
  • A. E. Castro-Ospina Tecnológico Metropolitano
  • S. Murillo Rendón Universidad Autónoma de Manizales
  • D. H. Peluffo-Ordóñez Universidad Técnica del Norte

DOI:

https://doi.org/10.29019/enfoqueute.v8n1.141

Keywords:

Case based reasoning, High dimensionality, Variable selection.

Abstract

Case-based reasoning (CBR) is a process used for computer processing that tries to mimic the behavior of a human expert in making decisions regarding a subject and learn from the experience of past cases. CBR has demonstrated to be appropriate for working with unstructured domains data or difficult knowledge acquisition situations, such as medical diagnosis, where it is possible to identify diseases such as: cancer diagnosis, epilepsy prediction and appendicitis diagnosis. Some of the trends that may be developed for CBR in the health science are oriented to reduce the number of features in highly dimensional data. An important contribution may be the estimation of probabilities of belonging to each class for new cases. In this paper, in order to adequately represent the database and to avoid the inconveniences caused by the high dimensionality, noise and redundancy, a number of algorithms are used in the preprocessing stage for performing both variable selection and dimension reduction procedures. Also, a comparison of the performance of some representative multi-class classifiers is carried out to identify the most effective one to include within a CBR scheme. Particularly, four classification techniques and two reduction techniques are employed to make a comparative study of multiclass classifiers on CBR

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References

Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI communications 7, 39-59.
Begum, S., Ahmed, M. U., Funk, P., Xiong, N., & Folke, M. (2011). Case-based reasoning systems in the health sciences: a survey of recent trends and developments. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 421-434.
Belkin, M. (2003). Problems of learning on manifolds. The University of Chicago.
Bhatia, S., Praveen , P., & Pillai, G. (2008). SVM based decision support system for heart disease classification with integer-coded genetic algorithm to select critical features. En Proceedings of the World Congress on Engineering and Computer Science, WCECS (págs. 22-24).
Bichindaritz, I. &. (1996). Temporal knowledge representation and organization for case-based reasoning.
Bichindaritz, I., & Conlon, E. (1996). Temporal knowledge representation and organization for case-based reasoning. Temporal Representation and Reasoning, 1996.(TIME'96), Proceedings., Third International Workshop on, 152-159.
Bichindaritz, I., & Marling, C. (2006). Case-based reasoning in the health sciences: What's next? Artificial intelligence in medicine, 127-135.
Gierl, L., Bull, M., & Schmidt, R. (1998). CBR in Medicine. En Case-Based Reasoning Technology (págs. 273-297). pringer Berlin Heidelberg.
Herrero, J. M. (2007). Una aproximación multimodal al diagnostico temporal mediante razonamiento basado en casos y razonamiento basado en modelos. Aplicaciones en la medicina.
Jenal, Gonzales, M., Alejo, S. M., & Ramos López, R. (2006). Sistema CBR para presentación de entrenamientos físicos personalizados en Internet.
Kolodner, J. (1983). Maintaining organization in a dynamic long‐term memory. Cognitive science 7, 243-280.
Kwiatkowska, M., & Atkins, M. (2004). Case representation and retrieval in the diagnosis and treatment of obstructive sleep apnea: a semio-fuzzy approach. En Proceedings of the 7th European Conference on Case-Based Reasoning (págs. 5-35).
Lozano, L., & Fernández, J. (2008). Razonamiento basado en casos: Una visión general.
Montani, S. (2008). Exploring new roles for case-based reasoning in heterogeneous AI systems for medical decision support. Applied Intelligence 28, 275-285.
Phuong, Hoang, N., Prasad, N., Hung, D. H., & Drake, J. (2001). pproach to combining case based reasoning with rule based reasoning for lung disease diagnosis. En IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th (págs. 883-888). IEEE.
Sheather, S. (2004). Density estimation. Statistical Science 19, 588-597.
Sundar, C., Chitradevi, M., & G. , G. (2012). Classification of cardiotocogram data using neural network based machine learning technique. International Journal of Computer Applications 47.
Trendowicz, A., & Jeffery, R. (2014). Software project effort estimation: Foundations and best practice guidelines for success. Springer.
Wang, Hsien-Tseng, & Tansel, A. U. (2013). MedCase: a template medical case store for case-based reasoning in medical decision support. En Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (págs. 962-967). ACM.

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Published

2017-02-24

How to Cite

Viveros-Melo, D., Ortega-Adarme, M., Blanco Valencia, X., Castro-Ospina, A. E., Murillo Rendón, S., & Peluffo-Ordóñez, D. H. (2017). Case based reasoning applied to medical diagnosis using multi-class classifier: A preliminary study. Enfoque UTE, 8(1), pp. 232–243. https://doi.org/10.29019/enfoqueute.v8n1.141

Issue

Vol. 8 No. 1 (2017): INCISCOS 2016, Special Edition

Section

Miscellaneous

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