Peer-reviewed article
Machine Learning for Predicting Neurodevelopmental Disorders in Children
Eugenia I. Toki, Ioannis G. Tsoulos, Vito Santamato, Jenny Pange ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study develops a machine learning model to predict neurodevelopmental disorders in children by analyzing speech and linguistic data from a serious game. The model was tested on 184 children aged around 7 years and achieved high accuracy in distinguishing between children with and without neurodevelopmental disorders. The researchers argue that this digital approach could enable earlier detection and more personalized interventions for at-risk children.
Key points
- A logistic regression model was applied to 184 children (average age 7 years) to predict neurodevelopmental disorders
- The model demonstrated high accuracy, precision, recall, and F1-score in distinguishing between children with and without neurodevelopmental disorders
- Data came from a serious game dataset collecting children's speech and linguistic responses
- Cluster analysis revealed distinct patterns in developmental data across physical, verbal, cognitive, and social-emotional domains
- The approach offers potential for early detection and personalized clinical assessment
Limitations
The document does not report specific accuracy metrics or performance numbers. There is no information about validation on independent datasets, comparison with clinical gold standards, or generalizability to different populations or settings.
Abstract
Developmental domains like physical, verbal, cognitive, and social-emotional skills are crucial for monitoring a child’s growth. However, identifying neurodevelopmental deficiencies can be challenging due to the high level of variability and overlap. Early detection is essential, and digital procedures can assist in the process. This study leverages the current advances in artificial intelligence to address the prediction of neurodevelopmental disorders through a comprehensive machine learning approach. A novel and recently developed serious game dataset, collecting various data on children’s speech and linguistic responses, was used. The initial dataset comprised 520 instances, reduced to 473 participants after rigorous data preprocessing. Cluster analysis revealed distinct patterns and structures in the data, while reliability analysis ensured measurement consistency. A robust prediction model was developed using logistic regression. Applied to a subset of 184 participants with an average age of 7 years, the model demonstrated high accuracy, precision, recall, and F1-score, effectively distinguishing between instances with and without neurodevelopmental disorders. In conclusion, this research highlights the effectiveness of the machine learning approach in diagnosing neurodevelopmental disorders based on cognitive features, and offers new opportunities for decision making, classification, and clinical assessment, paving the way for early and personalized interventions for at-risk individuals.
Findings the Hub has extracted
Each claim below is labelled by its type of design and links back to this report.
- Intervention
- Machine learning model using logistic regression applied to speech and linguistic data from a serious game
- Population
- 184 children with average age of 7 years
Key finding
The model demonstrated high accuracy, precision, recall, and F1-score in distinguishing between children with and without neurodevelopmental disorders