Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing
This work addresses the challenge of early ASD detection, particularly in children, by leveraging social media data, though it is incremental as it applies existing methods to a new dataset.
The study tackled the problem of diagnosing autism spectrum disorder (ASD) by using machine learning and deep learning models with natural language processing to analyze tweets, achieving an 88% accuracy rate in identifying texts from individuals with ASD.
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. A subset of 90,000 tweets was used for model training and testing. Results: Our AI models showed high accuracy, with an 88% success rate in identifying texts from individuals with ASD. Conclusion: The study demonstrates AI's potential in improving ASD diagnosis, especially in children, highlighting the importance of early detection.