Equilateral Active Learning (EAL): A novel framework for predicting autism spectrum disorder based on active fuzzy federated learning

Abstract

Autism Spectrum Disorder has a significant impact on society, and psychologists face a crucial challenge in identifying individuals with this condition. However, there is no definitive medical test for autism, and artificial intelligence can assist in diagnosis. A recent study outlines a framework for diagnosing autism spectrum disorders using Equilateral Active Learning (EAL). EAL incorporates three commonly used machine learning techniques: active learning, federated learning, and fuzzy deep learning. The framework integrates four robust datasets of children, teenagers, young adults, and adults using federated and fuzzy deep learning. Using EAL, autism spectrum disorder can be diagnosed with 90% accuracy, which is comparable to several machine learning methods, including statistical, traditional, modern, and fuzzy approaches.