J Med Syst DOI 10.1007/s10916-010-9594-9
ORIGINAL PAPER
Designing a Decision Support System for Distinguishing ADHD from Similar Children Behavioral Disorders Mona Delavarian & Farzad Towhidkhah & Parvin Dibajnia & Shahriar Gharibzadeh
Received: 7 April 2010 / Accepted: 6 September 2010 # Springer Science+Business Media, LLC 2010
Abstract In this study, a decision support system was designed to distinguish children with ADHD from other similar children behavioral disorders such as depression, anxiety, comorbid depression and anxiety and conduct disorder based on the signs and symptoms. Accuracy of classifying with Radial basis function and multilayer neural networks were compared. Finally, the average accuracy of the networks in classification reached to 95.50% and 96.62% by multilayer and radial basis function networks respectively. Our results indicate that a decision support system, especially RBF, may be a good preliminary assistant for psychiatrists in diagnosing high risk behavioral disorders of children. Keywords Attention deficit hyperactivity disorder . Conduct disorder . Anxiety disorder . Depression disorder . Comorbid depression and anxiety disorder . Artificial neural network . Classification
M. Delavarian : F. Towhidkhah : S. Gharibzadeh Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran P. Dibajnia Department of Psychiatry, Shahid Beheshti Medical University, Tehran, Iran S. Gharibzadeh (*) Neuromuscular Systems Laboratory, Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran e-mail:
[email protected]
Introduction A person with behavioral disorders is unable to provide suitable behavioral responses, according to her/his age, at different situations. The abnormal behaviors must be exhibited in at least two different places, and at least one of them must be school related. Thus, one of the important affected factors is educational performance [1]. Moreover, children with these disorders show antisocial behaviors. Thus, accurate diagnosis and treatment of this disorder during primary school ages is important. In this study, attention deficit and hyperactivity disorder (ADHD), conduct disorder, depression, anxiety and comorbid depression and anxiety were chosen from the multiple behavioral disorders, because of their high incidence among children and adolescents (Their prevalence have been reported: 35%, 5%, 1-6%, 1-9%, 20-50% of major depressed children, respectively [2]). Differential diagnosis of above-mentioned behavioral disorders is important and practically difficult due to their high similarities and comorbidity of their symptoms. It is estimated that 20 to 50% of children with major depressive disorder have an anxiety disorder. Generalized anxiety disorder most often coexists with other disorders such as depressive disorder [3]. Anxiety may appear itself by easy agitation and over activity. As Agency for Healthcare Research and Quality (AHRQ) announced, more than one-fourth of children with ADHD have one anxiety disorder [4]. Conduct and ADHD symptoms in children may exist at the same time [3]. Children with ADHD may exhibit impulsive and aggressive behaviors. These behaviors should be diagnosed and differentiated from conduct disorder symptoms. There is high risk of experiencing
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depression for both children with conduct disorder and ADHD. Sometimes conduct disorder reveals an underlying depression [3]. The other particularly important point is the differentiation between agitated depressive and ADHD, in which the continuous hyperactivity and restlessness might cause misdiagnosis [3]. Some researchers suggested that assessment of children with psychiatric disorders should be divided into different stages such as family interview, parental interview, interview with the child, obtaining other sources of information, and physical examination [5, 6]. These stages could help obtaining comprehensive information about a child’s behavior. Completing questionnaires could be useful as the first step. Since teachers have opportunities to observe and compare a large numbers of children, questionnaires that they complete could be a useful screening device [7]. Some researchers considered children behaviors at school and used teachers’ ratings for identification of students with behavioral problems [8, 9] and some of them relied on information obtained from the ratings of both parents and teachers [10]. In some studies, MRI and EEG were used in combination with other factors [11, 12]. Artificial Neural Network (ANN) is widely used in medical diagnosis as a nonlinear classification method [13]. In 1996, some researchers claimed that ANN is an efficient classifier for psychiatric disorders [14]. They used it in classification of schizophrenia and neurosis. The inputs of the neurons in neural networks are affected by the synaptic junction weights; the main purpose of the learning procedure is to adjust these weights [15]. In one research, ANN was applied for diagnosing antisocial personality disorder [24]. Some researchers detected presence of hopelessness and somatic complaints in depressed persons by using ANN. The network analyzed the relationship between nine symptoms of major depression [25]. Some evidences show that ANN is an appropriate method for evaluating psychological state, diagnosis of psychiatric disorders, and prediction of behavioral outcomes such as
Fig. 1 MLP neural network with two layers
suicide attempts, hospitalization, and death and other clinical decision problems [17]. Using Multi Layer Perceptron (MLP) neural network is more common in medical diagnosis. An original perceptron neural network has an input layer and at least two other layers (Fig. 1). Each layer of the perceptron neural network is an ordinary perceptron, and each ordinary perceptron can solve a simple logic problem [16]. These perceptrons together enable multilayer perceptron neural network to solve complicated problems [for more details see 17–19]. A multilayer ANN was applied for categorizing psychiatric disorders like schizophrenia, mania, depression and alcohol syndrome [26].The radial basis function (RBF) neural network consists of three layers: an input layer, a hidden radial basis function layer, and an output layer (Fig. 2). The weighted input of the RBF hidden layer is calculated by the Euclidian distance between the input vector and the weight matrix of links, which is connecting the input neurons to the hidden layer’s nodes, multiplied by a bias value [20– 23]. If the distance between the weight vector and the input vector increases, the output decreases and vice versa. Based on the data used for training the network to gain the most accurate results, the number of nodes in the hidden layer is found. Spread is another parameter in RBF neural network, which tunes the spread of the radial basis neurons. An RBF neural network was proposed in the detection of past suicide idea history as well as past self-harm history, as two potential risk factors of suicide commit [27]. In ANNs the weights and biases are obtained by training the neural network in order to minimize the misclassification error in a Mean Square Error (MSE) sense by using back propagation which is the most widely used training algorithm for ANN [for more details see 15]. After each randomly data presentation to the network, connection weights will be changed and modified to minimize the error rate and get the best result. The beneficial of a trained classifier is how it categorizes the class of subjects that has never been experienced.
J Med Syst Fig. 2 RBF neural network
The aim of this study is to design a suitable classifier for detection and categorization of mentioned behavioral disorders. For designing a classifier with high accuracy, radial basis function and multilayer perceptron neural networks are compared. The proposed system might have higher accuracy and speed and it is more efficient in screening out children with high risk of behavioral disorders.
Materials and methods Samples Our designed system required a large number of children with behavioral disorders such as ADHD, conduct disorder, anxiety disorder, depression and comorbid depression and anxiety disorders. For this purpose, 12 elementary schools were chosen, which cooperated with the team. 4 out of 12 primary schools were particularly for children with behavioral disorders, especially ADHD children. Children of these schools have at least 2 years experience of studying in common schools; then they were referred to these schools. 8 remaining elementary schools were public. Studying in these schools is almost free of charge, so the school population is very high. There are 5 grades in these schools and at least 3 classes for each grade. Each class has at least 25 students. The teachers of these schools were asked to refer children with remarkable behavioral problems in the past four months, to two experienced psychiatrists, which were selected. Teachers were asked to send a note to the
specialists for each referred student and explained their opinions about that student. Moreover, children behavioral questionnaire was filled for each student by the teacher. The two selective psychiatrists, who cooperated in diagnosis of children behavioral disorders, were expert pediatric psychiatrists. The students were referred to the psychiatrist with their parents and there, the psychiatrist evaluated children carefully. The two psychiatrists worked separately and both of them evaluated each child independently. They were asked to diagnose the disorders based on physical observation, child and parent’s interview, teacher’s report (by using Rutter’s behavioral disorders questionnaire) and if required, the study of student’s EEG According to the severity of the symptoms, behavioral characteristics of the children were reported and recorded in three grades from 0 to 2: “0” for never or one day each week during recent 6 months, “1” for occasionally (about 2, 3 or 4 days each week during recent 6 months) and “2” for most of the time (more than 4 days each week during recent 6 months). The duration of this process was about 7 months and 294 children, including 67 children with ADHD, 35 with conduct disorder, 52 with anxiety disorder, 37 depressed children, 33 with comorbid depression and anxiety and 70 children with normal behaviors were selected (Table 1). The aim of the project was explained to the parents and it was emphasized that the children information and the diagnosis made by the psychiatrists will be remained confidentially. Results will be used without revealing the name and family name and we will try not to interfere in student’s educational programs. Parents could prevent children from cooperation at any
Table 1 Number of subjects Normal
Conduct dis.
ADHD dis.
Anxiety dis.
Depression dis.
Comorbid depression & anxiety
Number of children
70
35
67
52
37
33
294
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time. The two mentioned psychiatrists determined normal and abnormal behaviors and the type of the behavioral disorders independently. The age range of the subjects was between 6 and 12 years old. It has to be mentioned that none of them had been under the medical treatments. In the next step, 68 severe repetitive signs and symptoms were extracted and collected in a form. These symptoms were collected from the combination of multiple sources of information such as data resulted from children behavior in school and home, and different tools like questionnaires
Table 2 Symptoms Age Sex Inattention Fidgeting with hands or feet or squirming in seat Hyperactivity, running excessively in inappropriate situation Disobedience Difficulty in awaiting turn Explosiveness or irritability Poor speech articulation Variableness and unpredictable mood Destruction of their own property or that of others Losing necessary thing for tasks and activities Bullying, Initiating physical fights toward peers Persistent lying Talking excessively Annoying and cruel behavior toward people Being easily fatigued Somatic complaints Restlessness and feeling keyed up or on edge Rejecting by other children Diminished ability to think or concentrate Excessive anxiety and worry Setting off to laughter or to tears easily Frequently biting nails or sucking thumb and fingers Tearing on arrival at school, refusing to go to school Stealing or acts of deceit Frequently truancy Annoying and cruelty to animals Sleep difficulties Poor self esteem, feeling worthless Weight and appetite changes Being solitary, reluctance to cooperate Hopelessness, pessimism about the future Psychomotor retardation Poor self-confidence, feelings of lack of competence Tearfulness Feeling guilty Hypervigilance
Table 3 Accuracy percentage with different number of hidden neuron Number of neurons
Accuracy (%)
20 19 18 17 16 15 14 13
87.64 89.88 91.01 93.25 94.38 95.50 93.25 91.01
12 11 10
94.38 92.13 89.88
(teacher and parents forms), interviewing and observing.. The 68 symptoms were for ADHD plus 4 other disorders such as child depression, anxiety, comorbid depression and anxiety and conduct disorders. Among these 68 symptoms, some of them had overlap with DSM-IV-TR criteria and some of them did not. Since 68 inputs devote a lot of time to check and reduce the speed of the system, 38 symptoms, which were most important in differentiation, were extracted by several independent experts in the field of child psychiatry. In the symptom extraction process, the experts were asked to score each symptom from 0 to 50, based on their importance in diagnosis. The remainder of 68 symptoms (30 items) were significantly less important than the 38 one, based on the opinion of specialists. Finally, the 38 symptoms, which had the highest average score, were selected and used as inputs for designing two classifiers: radial basis function and multilayer perceptron neural networks (Table 2). Except the input of age and sex, the value of each of the inputs was 0, 1, or 2. For
Table 4 Accuracy percentage with different extent of spread Extent of spread 1.5 2 2.5 3 3.5 3.03 4 4.5 5 5.5
Accuracy (%) 88.08 91.11 93.7 94.82 95.72 96.62 93.45 92.63 92.34 90.07
J Med Syst Fig. 3 RBF Training error function
determining the age input, patient’s age was normalized and in case of sex, for males 0 and for females 1 was considered. Development of classifier Randomly, 70% of dataset were used for training and the remained 30% were used as testing data; this random selection of the test and train data is repeated 100 times. Fig. 4 MLP Training error function
The neural network toolbox of MATLAB software was applied for this purpose. MLP and RBF neural networks were compared as classifiers in this study. 38 nodes, which are equal to the common characteristics of children with mentioned behavioral disorders, were used in the input layer of both classifiers. In both classifiers, the output layer consists of six neurons, encoding 6 classes of subjects with ADHD, conduct disorder, anxiety disorder, depression disorder,
J Med Syst Table 5 Number of correct and incorrect classified cases after classification with MLP
Table 7 Classification rate during testing with MLP neural network Class
ADHD Conduct dis. Anxiety dis. Depression dis. Comorbid dep.& anxiety Normal behavior
TP
TN
FP
FN
18 11 15 10 10 21
67 77 72 78 79 68
2 1 1 0 0 0
2 0 1 1 0 0
comorbid depression and anxiety disorder, and normal behavior. First, the connection weights were randomized in the range of −1 to +1. After the normalized inputs were entered to the input layer, the network outputs were compared with the real or desired outputs. Based on the amount of errors, the connection weights were changed by the back-propagation algorithm. This process was repeated until the amount of error reached to near 0.001. Finally, after the training phase completion, the weights were fixed to the final values. Then, the test data were applied to the network.
Results Random sampling cross-validation method was used in this study.The average accuracy of 100 times random selection of train and testing data is considered as the accuracy. The minimum error of the classifier was 0.001 after 1000 epochs. To find the best number of hidden neurons for MLP neural network different numbers of neurons were examined. The best results were gained by 15 and 31 hidden neurons for MLP and RBF networks, respectively. It is important to notice that a smaller number would result in less accurate results and a larger number would not improve the accuracy any further. The average result of classification by MLP achieved 95.50% with 15 hidden neurons (Table 3). To find the best amount of spread for RBF classifier,
Table 6 Number of correct and incorrect classified cases after classification with RBF
ADHD Conduct dis. Anxiety dis. Depression dis. Comorbid dep. & anxiety Normal behavior
TP
TN
FP
FN
19 11 15 11 9 21
68 77 72 78 79 68
1 1 1 0 0 0
1 0 1 0 1 0
ADHD Conduct Dis. Anxiety Depression Depression & anxiety Normal behavior
Testing accuracy [%] 90 100 93.75 90.90 100 100
different amount of spread was examined and spread of 3.03 was found to result sufficient convergence in minimizing the training and testing errors in RBF network. The accuracy of the RBF classifier with spread of 3.03 and 31 hidden neurons reached 96.62%. The accuracy percentage with different extent of spread can be seen in Table 4. The results of MLP and RBF training error, based on achieving minimum error, are shown in Figs. 3 and 4 respectively. As it is observed MLP training error reached about 0.008 after 1000 epochs that is more than RBF training error which is about 0.002. RBF training error is near 0.001. The number of correct and incorrect cases classified by both systems after testing is in Tables 5 and 6. The gained results in this study showed that RBF classifier is more accurate than MLP and categorizes the patients more precisely. According to the extracted symptoms, the RBF neural network classifier yields the following accuracies in the classification of the different above-mentioned classes. Accuracies of classification with both classifiers are shown in Tables 7 and 8. It shall be noted that training and validation of the system were done with Del XPS 1210 laptop- Genuine Intel (R) CPU, T2300 @ 1.66 GHz, 1.66 GHz 1.00 GB of RAM. In order to evaluate the performance of the designed system confusion matrix was applied. The sensitivity and specificity with both classifiers are shown in Tables 9 and 10. As it is observed the sensitivity of conduct disorder and normal behavior are 100% with both classifiers. Except the sensitivity of comorbid depression and anxiety group, the sensitivity, which is gained with RBF neural network, is
Table 8 Classification rate during testing with RBF neural network Class ADHD Conduct dis. Anxiety Depression Depression & anxiety Normal behavior
Testing accuracy [%] 95 100 93.75 100 90 100
J Med Syst Table 9 Sensitivity and specificity of the MLP neural network Class ADHD Conduct dis. Anxiety dis. Depression dis. Comorbid dep. & anx. Normal behavior
Sensitivity (%)
Specificity (%)
90 100 93.75 90.90 100 100
97.10 98.71 98.63 100 100 100
more than MLP neural network in this research. The specificities of depression disorder, comorbid depression anxiety and normal behavior are 100% with both classifiers. The specificities of anxiety disorder and conduct disorder with RBF neural network are more than specificities of them with MLP neural network. Just the specificity of the ADHD with MLP is more than specificity with RBF neural network. Therefore, the RBF classifier can categorize mentioned disorders with a high accuracy and efficiency.
Discussion Behavioral disorders increase the risk of mental disorders, learning disabilities, neuropsychological disorders and borderline personality and would affect children’s future life. Thus, early diagnosis of high-risk children is very important. High similarities between the symptoms of ADHD and some other children behavioral disorders like anxiety, depression, conduct disorder and comorbid depression and anxiety make their differential diagnosis difficult. Because of using different treatments, accurate diagnosis of these disorders would be important. Despite clinical progresses in diagnosing these disorders, there is still possibility of misdiagnosis. Many studies have tried to identify above-mentioned behavioral disorders among children by using different diagnostic methods and tools. Some researchers used multiple sources to gather the comprehensive information [5, 6]. Some of these sources are questionnaires, interviews
Table 10 Sensitivity and specificity of the RBF neural network Class ADHD Conduct dis. Anxiety dis. Depression dis. Comorbid dep. & anx. Normal behavior
Class sensitivity (%)
Specificity (%)
95 100 93.75 100 90 100
98.57 100 97.29 100 100 100
with family, parents and child and clinical observation. These studies have gathered comprehensive information but they considered much information simultaneously which takes a long time and also causes diagnostics errors. In some studies, teacher’s ratings were focused [8], so the risk of misdiagnosis was high. In some studies information obtained from both parents and teacher’s ratings were used for diagnosing behavioral disorders [10] but using this amount of information for diagnosis takes a long time and the risk of errors is high. MRI and EEG were applied for accurate diagnosis [11, 12]. Unfortunately, MRI is expensive and sensitivity of EEG is not high enough. ANN was used for classification of schizophrenia and neurosis [14]. In another study, this method was applied for diagnosing antisocial personality disorder [24]. MLP neural network was proposed for categorizing disorders such as schizophrenia, mania, depression and alcohol syndrome in adults [26]. These classifiers categorized the cases quickly but they did not useful for children behavioral disorders. A decision support system was designed for diagnosing ADHD [28]. They used different sources of information for designing this system. The system can detect ADHD quickly, based on information from different sources, but unfortunately it can be used just for ADHD diagnosis and it cannot differentiate the similar children behavioral disorders. Some researchers designed a decision support system for classification of patients with alcohol dependant, schizophrenia, depression, OCD based on ERP calculated responses [29]. The accuracy of classification for both patients with alcohol dependant and schizophrenia reached to 100% and for depression disorder and OCD classification reached to 91%. Unfortunately, this system with this much accuracy cannot be useful for children psychiatric disorders. In this research, a decision support system was trained and validated to assist the diagnosis of common children behavioral disorders. The amount of data sets plays an important role as too few training data sets make the network too complex and using too many data sets for training makes an over-trained network, which causes memorizing the unimportant features of the training set. Therefore, 294 referral children were selected. In all samples, the main complaint that led to referral children to specialists was their hyperactivity with different severity beside other symptoms. This system can distinguish children with ADHD from other common children behavioral disorders, with similar symptoms, in order to increase the accuracy of diagnosis. To design this system, two types of neural networks were compared: MLP and RBF. These networks are commonly used in non-linear medical classifications. RBF neural network was selected as a desirable system by categorizing 96.62% of the samples correctly in comparison with 95.5% classification accuracy
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obtained by MLP neural network. The RBF misclassifications were in distinguishing between ADHD and conduct (in one child), anxiety and ADHD (in one child), and anxiety and comorbid depression and anxiety (in one child). This limited number of diagnostic errors in comparison with errors which were done by the specialists, who were selected randomly from the list of medical council members, is worthfull as the average accuracy increased from 87.51%, without using decision support system, to 96.62% with using it. Based on the high accuracy of this classifier, this system can work as a decision support tool for psychiatrists. Besides, for more specific examination of high-risk children, this classifier can be used in schools as a reliable screening device in order to predict the behavioral disorders in primarily students. This proposed system tries to save time and cost, and increase the early diagnosis efficiency. Future prospect The proposed system is a prototype and it was done in a workspace with 294 data sets of children between 6 and 12 years. It is suggested that the designed network would be applied for a larger workspace as well as a group of subjects with a larger range of age. This will be very helpful to achieve a more reliable classifier. It is strongly recommended to use the information gained from EEG in this system in order to design a decision support system with more accurate diagnosis. It seems that using EEG can be a good beginning to increase the sensitivity and specificity of this decision support system.
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