Handwriting-Based ADHD Detection for Children Having ASD Using Machine Learning Approaches

Abstract

Attention deficit hyperactivity disorder (ADHD) for children is one of the behavioral disorders that affect the brain’s ability to control attention, impulsivity, and hyperactivity and its prevalence has increased over time. The cure for ADHD is still unknown and only early detection can improve the quality of life for children with ADHD. At the same time, children with ADHD often suffer from various comorbidities like autism spectrum disorder (ASD), major depressive disorder (MDD), etc. Various researchers developed computational tools to detect children with ADHD depending on handwriting text. Handwriting text-based systems are depending on a specific language that causes problems for non-native speakers of that language. Moreover, very few researchers considered other comorbidities such as ASD, MDD, etc., in their studies to detect ADHD for children. In this study, handwriting patterns or drawing is assumed as an aspect to identify/detect ADHD children who have ASD using machine learning (ML)-based approaches. We collected handwriting samples from 29 Japanese children (14 ADHD with coexisting ASD children and 15 healthy children) using a pen tablet. We asked each child to draw two patterns, namely zigzag lines (ZL) and periodic lines (PL) on a pen tablet and repeated them three times. We extracted 30 statistical features from raw datasets and these features were analyzed using sequential forward floating search (SFFS) and selected the best combinations or subsets of features. Finally, these selected features were fed into seven ML-based algorithms for detecting ADHD with coexisting ASD children. These classifiers were trained with leave-one-out cross-validation and evaluated their performances using accuracy, recall, precision, f1-score, and area under the curve (AUC). The experimental results showed that the highest performance scores (accuracy: 93.10%; recall: 90.48%; precision: 95.00%; f1-score: 92.68%; and AUC: 0.930) were achieved by the RF-based classifier for the PL predict task. This study will be helpful and provide evidence of the possibility of classifying ADHD children having ASD and healthy children based on their handwriting patterns.

Keywords

• ADHD
• ASD
• detection
• handwriting patterns
• machine learning