Early prediction of reading disability using machine learning.
This paper presents application of machine learning methods on a 356 sample dataset for early prediction of reading disability among first graders. A wide array of classifiers consisting of Support Vector Machines, Decision Trees (CART and C4.5), Linear Discriminant Analysis, k Nearest Neighbor and Naïve Bayes Classifiers were used in this study. Markov Blanket based feature selection algorithms (HITON-PC and HITON-MB) and wrapper based feature selection algorithms (forward, backward, forward and backward wrapping algorithm and support vector machine recursive feature elimination) were used to select the most relevant features for classification. The results indicate that an AUC score greater than 0.9 can be achieved using SVM classifiers even with a small set of demographics and screening variables. Moreover, a method for generating expert interpretable decision tree models from the high accuracy SVM models is also presented.
Varol
H Atakan
HA
Vanderbilt University, Nashville, TN, USA.
Mani
Subramani
S
Compton
Donald L
DL
Fuchs
Lynn S
LS
Fuchs
Douglas
D
D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D003663:Decision Trees; D016002:Discriminant Analysis; D004410:Dyslexia / Q000175:diagnosis*; D042241:Early Diagnosis; D006801:Humans; D011932:Reading