Evaluation of ANN and SVM classifiers as predictors to the diagnosis of students with learning disabilities

Due to the implicit characteristics of learning disabilities (LD), the identification or diagnosis of students with learning disabilities has long been a difficult issue. The LD diagnosis procedure usually involves interpreting some standard tests or checklist scores and comparing them to norms that are derived from statistical method. In this paper, we apply two well-known artificial intelligence techniques, artificial neural network (ANN) and support vector machine (SVM), to the LD diagnosis problem. To improve the overall identification accuracy, we also experiment with GA-based feature selection algorithms as the pre-processing step. To the best of our knowledge, this is the first attempt in applying ANN or SVM to similar application. The experimental results show that ANN in general performs better than SVM in this application, and the wrapper-based GA feature selection procedure can improve the LD identification accuracy, and among all, the combination of using SVM learner in the feature selection procedure and ANN learner in the classification stage results in feature set that achieves the best prediction accuracy. Most important of all, the study indicates that the ANN classifier can correctly identify up to 50% of the LD students with 100% confidence, which is much better than currently used LD diagnosis predictors derived through the statistical method. Consequently, a properly trained ANN classification model can be a strong predictor for use in the LD diagnosis procedure. Furthermore, a well-trained ANN model can also be used to verify whether a LD diagnosis procedure is adequate. In conclusion, we expect that AI techniques like ANN or SVM will certainly play an essential role in future LD diagnosis applications. (c) 2007 Elsevier Ltd. All rights reserved.