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

Sergio Rubio-Martín; María Teresa García-Ordás; Martín Bayón-Gutiérrez; Natalia Prieto-Fernández; José Alberto Benítez-Andrades

doi:10.1007/s13755-024-00281-y

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

Health Inf Sci Syst. 2024 Mar 6;12(1):20. doi: 10.1007/s13755-024-00281-y. eCollection 2024 Dec.

Authors

Sergio Rubio-Martín^#¹, María Teresa García-Ordás^#², Martín Bayón-Gutiérrez^#², Natalia Prieto-Fernández^#², José Alberto Benítez-Andrades^#¹

Affiliations

¹ SALBIS Research Group, Dept. of Electric, Systems and Automatics Engineering, Universidad de León, Campus of Vegazana s/n, 24071 León, León Spain.
² SECOMUCI Research Group, Dept. of Electric, Systems and Automatics Engineering, Universidad de León, Campus of Vegazana s/n, 24071 León, León Spain.

^# Contributed equally.

Abstract

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.

Keywords: ASD; Deep Learning; Machine Learning; NLP; Tweets.