Abstract
Genetic Algorithms (GAs) is one of the most effective technique applied to feature selection in medical diagnostic decisions. In particular, Thalassemia, which is one of the most common genetic disorders found around the world. The main problems of diagnosing this disease are the complex processes for identifying the several types of Thalassemia. Moreover, diagnostic methods are slow and rely on expert knowledge and experience as well as expensive equipment. For these reasons, in this study, a new framework of applied DBN and BLR (MCMC)-GAs-KNN for Thalassemia Expert System is proposed. The filter techniques called DBNs and the hybrid classification technique namely BLR (MCMC)-GAs-KNN will be used for classifying the types of β-Thalassemia. The obtained result will be compared to the results of other techniques such as BNs, BLR based on Classical (ML) and Bayesian (MCMC) approach, SVM, MLP, KNN, C5.0, and CART for selecting the best results to implement Thalassemia Expert System.
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Paokanta, P. (2012). DBNs-BLR (MCMC) -GAs-KNN: A Novel Framework of Hybrid System for Thalassemia Expert System. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_33
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DOI: https://doi.org/10.1007/978-3-642-34478-7_33
Publisher Name: Springer, Berlin, Heidelberg
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