Attention Deficit Hyperactivity Disorder (ADHD) Student Classification Using Genetic Algorithm and Artificial Neural Network S. Yenaeng 1, S. Saelee 2 and S. Krootjohn 2 1 Department of Computer Education, Faculty of Education, Bansomdejchaopraya Rajabhat University, Bangkok, Thailand 2 Department of Computer Education, Faculty of Technical Education, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

Contents I. PROBLEM STATEMENT II. SOLVE THE PROBLEM III. DATA SET IV. GANN APPROACH TO FEATURE SELECTION V. EXPERIMENTAL DESIGN VI. CONCLUSION

I. PROBLEM STATEMENT  What is Attention deficit hyperactivity disorder (ADHD) ?

I. PROBLEM STATEMENT  What is Attention deficit hyperactivity disorder (ADHD) ? It is a group of neurobehavioral disorders that have a neurobiological basis with strong genetic components.

I. PROBLEM STATEMENT  It impacts multiple areas of brain functions and life activities

I. PROBLEM STATEMENT  Clinical interviews and multiple informants both contribute to a reliable assessment of ADHD

I. PROBLEM STATEMENT  Owing to the complex nonlinear relationship among the main morphological features of ADHD, it is difficult to describe by the traditional linear regression methods.

II. SOLVE THE PROBLEM  Among a great variety of classification Techniques suggested so far for medical diagnosis neural network (NN) has been one of the most popular methods that consistently demonstrated its strengths and potentials in solving practical classification problems

II. SOLVE THE PROBLEM  There exist redundant or useless information in these features. In this case, it is essential to carry out the task of feature selection, a technique commonly used in machine learning for selecting a subset of relevant features for building a robust learning model. Feature selection can discover the optimum feature subset which is rich in relevant information.

III. DATA SET  The sample was drawn from 5 primary schools in the area of Bangkok.  Our teacher rating scale was composed of 30 attributes and was derived from the ADHD: KUS-SI Rating Scales.  ADHD dataset contains 4 classes and 1,000 students, of which 115 cases were ADHD and 885 cases were control.  We divided the dataset into two parts, of which one is the training set (700 samples) is used for training the parameter of NN using a BP algorithm.

III. DATA SET  The testing set (300 samples) is introduced for testing the ability of neural network.  The number of attributes is reduced to 21 attributes using Genetic Search.  The reduced data set is fed to the GANN models. K-fold cross validation method is used as the test mode.

III. DATA SET Table 1: Attributes list and description

IV. GANN APPROACH TO FEATURE SELECTION  The genetic search starts with zero attributes, and an initial population with randomly generated rules.  The process of generation continues until it evolves a population P where every rule in P satisfies the fitness threshold.  Initial population of 20 instances, generation continued till t­he eightieth generation with cross over probability of 0.6 and mutation probability of 0.033

IV. GANN APPROACH TO FEATURE SELECTION  The genetic search resulted in twenty-one attributes out of thirty attributes is show in Table 2 and 3.

IV. GANN APPROACH TO FEATURE SELECTION Figure 1: Overview of the GANN

IV. GANN APPROACH TO FEATURE SELECTION Neural Network as ADHD Classifier  Classification is a supervised learning method to extract models describing important data classes or to predict future trends.  Our work intends to use Artificial Neural Network (NN) to diagnosis the presence of ADHD in students.

IV. GANN APPROACH TO FEATURE SELECTION Neural Network as ADHD Classifier  The learning algorithm of NN is a supervised learning method by training feed forward neural network using error back propagation technique to determine the parameters of neural network.

V. EXPERIMENTAL DESIGN Data and Variables  The sample was drawn from 5 primary schools in the area of Bangkok. Our teacher rating scale was composed of 30 attributes and was derived from the ADHD: KUS-SI Rating Scales.  This ADHD dataset contains 4 classes and 1,000 students, of which 115 cases were ADHD and 885 cases were control.  We divided the dataset into two parts, of which one is the training set (700 samples) is used for training the parameter of NN using a BP algorithm. On the other hand, the testing set (300 samples) is introduced for the purpose of testing the generalization ability of neural network.

V. EXPERIMENTAL DESIGN Experimental results and Performance analysis  Experiments were conducted with Weka tool. Data set of 1,000 records with 30 attributes is used.  We have trained the classifiers to classify the ADHD data set. The general and specific confusion matrix of four classes (Y1, Y2, Y3, and Y4) is show in Table 4 and 5.

V. EXPERIMENTAL DESIGN Experimental results and Performance analysis

V. EXPERIMENTAL DESIGN Experimental results and Performance analysis  As far as the classification performance of the model is concerned, the classification rate (C) denotes the percentage of correctly classified samples, which is computed by the following formula (Huang et al, 2011).

V. EXPERIMENTAL DESIGN  As the results in Tables 6 show that we obtained 21 features selected by our GA approach.  It can be obviously seen that the recognition rate in terms of the right classification percentage has distinctly increased, which is measured by GANN model has the classification accuracy is 81%.  The modeling time using GANN technique is also greatly reduced. Experimental results and performance analysis

VI. CONCLUSION & SUGGESTION  We propose a machine learning technique where NN as classifier is combined with GA approach to classify more accurately the presence of ADHD with reduced number of attributes.  Verify the effectiveness of NN classifier and GANN are adopted in the same experimental conditions to study classification performance of the samples for the testing sets.  The experiment shows that thirty attributes are reduced to 21 attributes using GANN approach while not allowing the accuracy of the classifier to decrease  When classify ADHD student can be taught to children is different from the normal group.