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A comparison of classification models to identify the Fragile X Syndrome

Author

Listed:
  • Rafael Pino-Mejias
  • Mercedes Carrasco-Mairena
  • Antonio Pascual-Acosta
  • Maria-Dolores Cubiles-De-La-Vega
  • Joaquin Munoz-Garcia

Abstract

The main models of machine learning are briefly reviewed and considered for building a classifier to identify the Fragile X Syndrome (FXS). We have analyzed 172 patients potentially affected by FXS in Andalusia (Spain) and, by means of a DNA test, each member of the data set is known to belong to one of two classes: affected, not affected. The whole predictor set, formed by 40 variables, and a reduced set with only nine predictors significantly associated with the response are considered. Four alternative base classification models have been investigated: logistic regression, classification trees, multilayer perceptron and support vector machines. For both predictor sets, the best accuracy, considering both the mean and the standard deviation of the test error rate, is achieved by the support vector machines, confirming the increasing importance of this learning algorithm. Three ensemble methods - bagging, random forests and boosting - were also considered, amongst which the bagged versions of support vector machines stand out, especially when they are constructed with the reduced set of predictor variables. The analysis of the sensitivity, the specificity and the area under the ROC curve agrees with the main conclusions extracted from the accuracy results. All of these models can be fitted by free R programs.

Suggested Citation

  • Rafael Pino-Mejias & Mercedes Carrasco-Mairena & Antonio Pascual-Acosta & Maria-Dolores Cubiles-De-La-Vega & Joaquin Munoz-Garcia, 2008. "A comparison of classification models to identify the Fragile X Syndrome," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(3), pages 233-244.
    • Handle: RePEc:taf:japsta:v:35:y:2008:i:3:p:233-244
    DOI: 10.1080/02664760701832976
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    Cited by:

    1. Nader Fallah & Arnold Mitnitski & Kenneth Rockwood, 2011. "Applying neural network Poisson regression to predict cognitive score changes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 2051-2062, November.
    2. Yanhong Luo & Zhi Li & Husheng Guo & Hongyan Cao & Chunying Song & Xingping Guo & Yanbo Zhang, 2017. "Predicting congenital heart defects: A comparison of three data mining methods," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    3. Alexander Herr, 2010. "Statistics for Categorical Surveys—A New Strategy for Multivariate Classification and Determining Variable Importance," Sustainability, MDPI, vol. 2(2), pages 1-18, February.
    4. Jian Zhou & Xibing Li & Hani Mitri, 2015. "Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(1), pages 291-316, October.

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