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Learning Disabilities

Learning Disabilities

Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...

Intellectual Disability

Intellectual Disability

Intellectual disability (ID) is a neurodevelopmental condition characterized by deficits in intellectual and adaptive functioning that manifest during the developmental period. This condition encompasses challenges in reasoning, memory, problem-solving, and learning, accompanied by impairments in everyday life skills, such as communication, self-care, and social interactions. Intellectual disability affects approximately 1% of the population in the United States, impacting an estimated 5...

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Related Experiment Video

Updated: May 31, 2026

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Explainable AI for early developmental disability detection: a neuro-fuzzy approach.

Adel Saber Alanazi¹, Sohil Alqazlan², Rayan Alanazi³

¹College of Education, Jouf University, Sakakah, Saudi Arabia.

Frontiers in Public Health

|May 29, 2026

Summary

This summary is machine-generated.

An Adaptive Neuro-Fuzzy Inference System (ANFIS) shows high accuracy in identifying developmental disabilities in children. This AI tool offers a transparent and interpretable approach for clinical decision support.

Keywords:

Adaptive Neuro-Fuzzy Inference System (ANFIS)autism spectrum disorder developmental disabilities early intervention explainable AI fuzzy logic interpretable machine learning pediatrics diagnosis

Related Experiment Videos

Last Updated: May 31, 2026

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Area of Science:

Pediatric Health
Artificial Intelligence in Medicine
Developmental Neuroscience

Background:

Developmental disabilities impact 1 in 6 children, with diagnosis delays disproportionately affecting underserved populations.
Traditional screening methods lack the transparency needed for clinical integration.
Early identification and intervention are crucial for improving outcomes in children with developmental disabilities.

Purpose of the Study:

To develop and validate an interpretable AI model for early screening of developmental disabilities.
To assess the diagnostic accuracy and clinical utility of the Adaptive Neuro-Fuzzy Inference System (ANFIS).
To address the limitations of traditional machine learning in clinical settings.

Main Methods:

An Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed using a longitudinal dataset of 5,000 children (ages 1-6).
The model incorporated Gaussian membership functions, Sugeno fuzzy inference, and age-adjusted ratios.
Performance was evaluated using standard classification metrics and five-fold cross-validation.

Main Results:

Significant differences in cognitive, behavioral, motor, and social interaction scores were observed between diagnostic groups (p < 0.001).
The ANFIS model achieved 96.0% accuracy, 87.5% sensitivity, and 97.6% specificity on a test set.
Cross-validation yielded a mean accuracy of 89.2% ± 3.4%, with Cognitive_Social_Ratio being the most influential indicator.

Conclusions:

The ANFIS approach offers a clinically relevant balance of accuracy and interpretability for decision support.
This AI tool shows promise as a viable clinical decision-support system for developmental disabilities.
Further validation in diverse populations is recommended before widespread clinical implementation.