The Role of Machine Learning in Autism Detection

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects social interaction, communication, and behaviour. Early diagnosis is crucial for timely intervention, yet traditional diagnostic methods are subjective, time-consuming, and often inaccessible in remote areas. This research explores the use of machine learning (ML) techniques to enhance autism detection through data-driven approaches. Various supervised learning algorithms, including Support Vector Machines (SVM), Decision Trees, Random Forest, and Deep Learning models, are evaluated using ASD screening datasets. Additionally, we examine unsupervised and reinforcement learning methods to detect ASD traits in children and adults. The results indicate that ML-based models can achieve high accuracy, offering a scalable, cost-effective, and objective alternative to conventional diagnosis methods.

Introduction

Autism Spectrum Disorder (ASD) is a lifelong condition marked by social communication challenges and repetitive behaviors, affecting about 1 in 100 children worldwide. Traditional diagnosis methods like ADOS and M-CHAT depend on expert behavioral assessments, which can delay diagnosis due to limited specialist availability. Early diagnosis is critical for effective intervention.

Machine learning (ML) offers promising alternatives by analyzing large datasets to detect ASD traits more quickly and objectively. Techniques include supervised learning (Decision Trees, SVM, Random Forest), deep learning (CNNs, RNNs), and multi-modal data fusion combining behavioral, speech, eye-tracking, and genetic data.

ML-based applications include mobile screening tools, AI-driven chatbots, clinical decision support systems, neuroimaging analysis, behavior and emotion recognition, and personalized educational platforms.

The methodology involves using public ASD datasets, data preprocessing, and employing various ML models with performance measured by accuracy, precision, recall, and ROC-AUC. Surveys among professionals and parents confirm awareness of ASD and the benefits of ML, highlighting privacy and ethical concerns.

Challenges include limited and imbalanced datasets, data privacy, labeling difficulties, data heterogeneity, model overfitting, and biases. Proposed solutions involve transfer learning, federated learning, semi-supervised learning, and crowdsourced labeling to improve data quality and model reliability while addressing privacy and ethical issues.

Conclusion

The Autism Detection System using Machine Learning (ML) represents a significant advancement in early autism screening, offering fast, accurate, and scalable predictions. By leveraging AI-driven assessment techniques, this project enhances diagnostic accuracy, reduces healthcare costs, and improves accessibility for both doctors and individuals seeking autism evaluations. A. Machine learning in Autism detection provides the following benefits 1) Improved Early Detection • Faster and more accurate than traditional manual assessments. • Enables timely intervention and support for individuals at risk. 2) Scalability & Sustainability • Cloud-based and API-driven architecture makes it highly scalable. 3) Economic & Social Benefits • Reduces the burden on healthcare professionals by automating initial screening. • Provides remote accessibility, making autism screening available worldwide. 4) Ethical & Privacy Considerations • Uses secure data encryption and GDPR/HIPAA-compliant privacy measures. • Ensures bias-free AI models by training on diverse datasets. By bridging the gap between AI and healthcare, this project has the potential to positively impact millions of lives, especially in rural and underprivileged communities. B. Future Work for Autism Detection Using Machine Learning To enhance the accuracy, scalability, and usability of the ML-based Autism Detection System, the following future improvements and research directions proposed: 1) Enhancing Model Accuracy & Performance Multi-Modal Machine Learning • Integrate speech analysis, facial expression detection, and eye-tracking data to improve autism detection. Personalized AI Models • Develop AI models that adapt to individual differences in autism symptoms. • Train models with diverse datasets across different age groups, cultures, and demographics. 2) Expanding Data Collection & Diversity -Incorporating Real-World Data • Use wearable devices &IoT sensors to collect behavioural data. • Partner with hospitals and research centres for large-scale clinical validation. -Longitudinal Studies for Early Prediction • Collect and analyze long-term patient data to predict autism progression over time. 3) Advanced Deployment & Integration Mobile App & Wearable Device Integration • Develop AI-powered autism detection mobile apps for home-based screening. • Integrate with smartwatches and eye-tracking glasses to monitor social interactions. Telemedicine and Hospital System Integration • Implement API-based integration with hospital management systems for seamless patient referrals. • Enable virtual autism assessments via video conferencing platforms. 4) Expanding Research & Global Adoption Cross-Cultural Autism Screening • Validate the model in different countries to account for cultural differences in autism symptoms. AI-Powered Autism Treatment Recommendations • Develop AI models that not only detect autism but also suggest therapy & intervention plans. • Use reinforcement learning to create personalized treatment strategies.

References

[1] Lord, C., et al. (2000). \"The Autism Diagnostic Observation Schedule (ADOS).\" Journal of Autism and Developmental Disorders. https://link.springer.com/article/10.1023/A:1005592401947 [2] Thabtah, F. (2018). \"Machine Learning in Autism Screening: A Review.\" International Journal of Medical Informatics. https://journals.sagepub.com/doi/abs/10.1177/1460458218824711 [3] Heinsfeld, A. S., et al. (2018). \"Identification of Autism Spectrum Disorder Using Deep Learning.\" NeuroImage. https://www.sciencedirect.com/science/article/pii/S1053811917306642 [4] Autism Screening Tools With Machine Learning and Deep Learning https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119841937.ch10 [5] Autism screening: an unsupervised machine learning approach https://www.semanticscholar.org/paper/Autism-screening%3A-anunsupervised-machine-learning-Thabtah-Spencer/af78db48843b23647d14040afe5a4c19d1b5d80d

Copyright

Copyright © 2025 Zainab Lakhani . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.