Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

Abstract

Attention deficit hyperactivity disorder (ADHD) is one of childhood’s most frequent neurobehavioral disorders. The purpose of this study is to: (i) extract the most prominent risk factors for children with ADHD; and (ii) propose a machine learning (ML)-based approach to classify children as either having ADHD or healthy. We extracted the data of 45,779 children aged 3–17 years from the 2018–2019 National Survey of Children’s Health (NSCH, 2018–2019). About 5218 (11.4%) of children were ADHD, and the rest of the children were healthy. Since the class label is highly imbalanced, we adopted a combination of oversampling and undersampling approaches to make a balanced class label. We adopted logistic regression (LR) to extract the significant factors for children with ADHD based on p-values (<0.05). Eight ML-based classifiers such as random forest (RF), Naïve Bayes (NB), decision tree (DT), XGBoost, k-nearest neighborhood (KNN), multilayer perceptron (MLP), support vector machine (SVM), and 1-dimensional convolution neural network (1D CNN) were adopted for the prediction of children with ADHD. The average age of the children with ADHD was 12.4 ± 3.4 years. Our findings showed that RF-based classifier provided the highest classification accuracy of 85.5%, sensitivity of 84.4%, specificity of 86.4%, and an AUC of 0.94. This study illustrated that LR with RF-based system could provide excellent accuracy for classifying and predicting children with ADHD. This system will be helpful for early detection and diagnosis of ADHD.

Optimal Channels and Features Selection Based ADHD Detection From EEG Signal Using Statistical and Machine Learning Techniques

“…Over 40% of children and younger who suffer from ADHD develop behavioral problems until adults [6], [7], leading to serious problems [8]. ADHD is also significantly associated with comorbidities like asthma, depression, anxiety, and learning difficulties [9], [10]. Males are more likely to have ADHD than females and their behavior differs [11].…”

“…Nowadays, the efficient diagnosis of children with ADHD is still a major problem. Various research works have been carried out to propose an automated system for early diagnosis of children with ADHD [10], [12]- [14]. There is still a scope to propose an automated system for the early detection and classification of children with ADHD.…”

“…Feature selection is very important because it removes redundant features, improves the model's performance, and reduces overfitting problems. In the literature, there were various feature selection techniques such as principal component analysis (PCA) [49], [51], minimum redundancy maximum relevance (mRMR) [52], MI [53], t-test [4], [25], support vector machine recursive elimination (SVM-RFE) [52], least absolute shrinkage and selection operator (LASSO) [25], logistic regression (LR) [10], and so on, which were widely used to select the potential features in any domain like ADHD. For example, Tenev et al [54] used the forward selection method to select the significant features for ADHD.…”

“…, ML-based approaches have been widely used in the field of medical imaging [46], time series [20], [21], [55], [56], and bioinformatics [57]. Like other domains, ML-based approaches were also widely used to discriminate children with ADHD from healthy control [4], [10], [13], [24], [25], [47], [49], [52], [58]- [60]. For example, Muller et al [61] proposed SVM-based children with ADHD detection system.…”

Development of Observation Instruments for Partnership Model-Based Learning Activities in Distance Learning in Elementary Schools

“…Positive student activity is when students actively discuss with teachers and fellow students to solve problems, submit ideas or opinions, and do assignments given by teachers, and others (Krasnova & Ananjev, 2015). Negative student activities include noisy students, disturbing friends, not paying attention to the teacher's explanation, and doing something that has nothing to do with the ongoing teaching and learning process (Maniruzzaman et al, 2022). To measure and assess student learning activities in the learning process, observation sheets of student learning activities are needed.…”