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

• Published in: Health information science and systems 2024-03, Vol.12 (1), p.20, Article 20
• Main Authors: Rubio-Martín, Sergio, García-Ordás, María Teresa, Bayón-Gutiérrez, Martín, Prieto-Fernández, Natalia, Benítez-Andrades, José Alberto
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial intelligence; Autism; Bioinformatics; Classification; Computational Biology/Bioinformatics; Computational linguistics; Computer Science; Datasets; Decision trees; Deep learning; Diagnosis; Health Informatics; Improving healthcare services with IT and computer-based systems; Information Systems and Communication Service; Language processing; Machine learning; Medical research; Medicine, Experimental; Model accuracy; Natural language interfaces; Natural language processing; Neural networks; Prediction models; Recurrent neural networks; Social media; Social networks
• ISSN: 2047-2501; 2047-2501
• DOI: 10.1007/s13755-024-00281-y

Purpose The main aim of our study was to explore the utility of artificial intelligence (AI) in diagnosing autism spectrum disorder (ASD). The study primarily focused on using machine learning (ML) and deep learning (DL) models to detect ASD potential cases by analyzing text inputs, especially from social media platforms like Twitter. This is to overcome the ongoing challenges in ASD diagnosis, such as the requirement for specialized professionals and extensive resources. Timely identification, particularly in children, is essential to provide immediate intervention and support, thereby improving the quality of life for affected individuals. Methods We employed natural language processing (NLP) techniques along with ML models like decision trees, extreme gradient boosting (XGB), k-nearest neighbors algorithm (KNN), and DL models such as recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), bidirectional encoder representations from transformers (BERT and BERTweet). We extracted a dataset of 404,627 tweets from Twitter users using the platform’s API and classified them based on whether they were written by individuals claiming to have ASD (ASD users) or by those without ASD (non-ASD users). From this dataset, we used a subset of 90,000 tweets (45,000 from each classification group) for the training and testing of these models. Results The application of our AI models yielded promising results, with the predictive model reaching an accuracy of almost 88% when classifying texts that potentially originated from individuals with ASD. Conclusion Our research demonstrated the potential of using AI, particularly DL models, in enhancing the accuracy of ASD detection and diagnosis. This innovative approach signifies the critical role AI can play in advancing early diagnostic techniques, enabling better patient outcomes and underlining the importance of early identification of ASD, especially in children.