Employing Classification Techniques on SmartSpeech Biometric Data towards Identification of Neurodevelopmental Disorders

• Published in: Signals (Basel) Vol. 4; no. 2; pp. 401 - 420
• Main Authors: Toki, Eugenia I., Tatsis, Giorgos, Tatsis, Vasileios A., Plachouras, Konstantinos, Pange, Jenny, Tsoulos, Ioannis G.
• Format: Journal Article
• Language: English
• Published: Tokyo MDPI AG 01.05.2023
• Subjects: Accuracy; Algorithms; Attention deficit hyperactivity disorder; Autism; Biomarkers; Classification; Cognition & reasoning; Cognitive ability; Communication; Decision making; Deep learning; deep learning neural networks; Eye movements; Heart rate; Language disorders; Machine learning; machine learning optimizers; Neural networks; Neurodevelopmental disorders; Problem solving; radial basis function neural network; SmartSpeech
• ISSN: 2624-6120; 2624-6120
• DOI: 10.3390/signals4020021

Early detection and evaluation of children at risk of neurodevelopmental disorders and/or communication deficits is critical. While the current literature indicates a high prevalence of neurodevelopmental disorders, many children remain undiagnosed, resulting in missed opportunities for effective interventions that could have had a greater impact if administered earlier. Clinicians face a variety of complications during neurodevelopmental disorders’ evaluation procedures and must elevate their use of digital tools to aid in early detection efficiently. Artificial intelligence enables novelty in taking decisions, classification, and diagnosis. The current research investigates the efficacy of various machine learning approaches on the biometric SmartSpeech datasets. These datasets come from a new innovative system that includes a serious game which gathers children’s responses to specifically designed speech and language activities and their manifestations, intending to assist during the clinical evaluation of neurodevelopmental disorders. The machine learning approaches were used by utilizing the algorithms Radial Basis Function, Neural Network, Deep Learning Neural Networks, and a variation of Grammatical Evolution (GenClass). The most significant results show improved accuracy (%) when using the eye tracking dataset; more specifically: (i) for the class Disorder with GenClass (92.83%), (ii) for the class Autism Spectrum Disorders with Deep Learning Neural Networks layer 4 (86.33%), (iii) for the class Attention Deficit Hyperactivity Disorder with Deep Learning Neural Networks layer 4 (87.44%), (iv) for the class Intellectual Disability with GenClass (86.93%), (v) for the class Specific Learning Disorder with GenClass (88.88%), and (vi) for the class Communication Disorders with GenClass (88.70%). Overall, the results indicated GenClass to be nearly the top competitor, opening up additional probes for future studies toward automatically classifying and assisting clinical assessments for children with neurodevelopmental disorders.