Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing

arXiv:2403.03581v1 Announce Type: cross
Abstract: Purpose: Our study explored the use of artificial intelligence (AI) to diagnose autism spectrum disorder (ASD). It focused on machine learning (ML) and deep learning (DL) to detect ASD from text inputs on social media, addressing challenges in traditional ASD diagnosis.
Methods: We used natural language processing (NLP), ML, and DL models (including decision trees, XGB, KNN, RNN, LSTM, Bi-LSTM, BERT, and BERTweet) to analyze 404,627 tweets, classifying them based on ASD or non-ASD authors. …

abstract accuracy artificial artificial intelligence arxiv autism challenges cs.cl cs.lg deep learning detection inputs intelligence language language processing machine machine learning media natural natural language natural language processing processing social social media spectrum study text type