A comparison of classification models to identify the Fragile X Syndrome

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.fragile X syndrome, support vector machines, multilayer perceptron, classification trees, logistic regression, ensemble methods, R system,

A screening algorithm for HIV‐associated neurocognitive disorders

“…SVM methods have been increasingly used in a wide variety of medical classification problems. In certain instances they can prove superior in terms of classification accuracy to standard methods such as logistic regression, especially in being able to extract key predictors [18][19][20][21] that can then be used in the simplified algorithm.…”

“…The SVM has been shown to be extremely robust in solving prediction problems while handling large sets of predictors [18]. It is an alternative to more standard statistical techniques such as logistic regression and in certain situations has been found to be superior to logistic regression for finding a robust fit with fewer predictors [18][19][20][21]. …”

Statistics for Categorical Surveys—A New Strategy for Multivariate Classification and Determining Variable Importance

“…In a recent study, support vector machines, a machine learning classification method, showed best performance when compared with other supervised learning methods including decision trees [29]. However, their work was concerned with a binary automated classification and the performance of the bagged support vector machines improved with the reduction of predictors.…”

Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction

“…The use of SL algorithms for the development of predictive and descriptive data mining models has become widely accepted in mining and geotechnical applications, promising powerful new tools for practicing engineers (Garzón et al 2006;Berrueta et al 2007;Sakiyama et al 2008;Pino-Mejías et al 2008, 2010Pozdnoukhov et al 2009;Tesfamariam and Liu 2010;Zhou et al 2011Zhou et al , 2012Zhou et al , 2013González-Rufino et al 2013;Liu et al 2013Liu et al , 2014. Numerous approaches for PS prediction have been developed based on different SL techniques during recent decades (Tawadrous and Katsabanis 2007;Zhou et al 2011;Wattimena et al 2013;Ghasemi et al 2014a, b).…”