The effeciency of data types for classification performance of machine learning techniques for screening β-Thalassemia

Patcharaporn Paokanta, Michele Ceccarelli, Somdat Srichairatanakool

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Performance of classification methods using Machine Learning Techniques majority depends on the quality of data were used in learning. The transformation techniques are used to increase the efficiency of classification because each type of data is suitable for different classification techniques. This study is aimed at providing comparative performance of different classification techniques by changing the type of data to find the appropriate type of data for each technique. The β-Thalassemia data is used for classifying genotypes of β-Thalassemia patients. The results of this study show that the types of data are Nominal scale which can be used as well for Bayesian Networks (BNs) and Multinomial Logistic Regression with the percentage of accuracy 85.83 and 84.25 respectively. Moreover, the data types which such as Interval scale can be used appropriately for K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP) and NaiveBayes with the percentage of accuracy 88.98, 87.40 and 84.25 respectively. In the future, we will study the impacts of data separation to be used for classifying genotypes of patients with Thalassemia using the other classification techniques.

Original languageEnglish
Title of host publication2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010
DOIs
Publication statusPublished - 1 Dec 2010
Externally publishedYes
Event2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010 - Roma, Italy
Duration: 7 Nov 201010 Nov 2010

Other

Other2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010
CountryItaly
CityRoma
Period7/11/1010/11/10

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Keywords

  • β-Thalassemia
  • Bayesian networks
  • Classification techniques
  • K-nearest neighbors
  • Multi-layer perceptron
  • Multinomial logistic regression
  • NaiveBayes

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

Cite this

Paokanta, P., Ceccarelli, M., & Srichairatanakool, S. (2010). The effeciency of data types for classification performance of machine learning techniques for screening β-Thalassemia. In 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010 [5702769] https://doi.org/10.1109/ISABEL.2010.5702769