A systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunities

2026-08-18

AI Summary

该研究通过系统回顾55篇2017至2023年的文献，探讨了机器学习技术在自闭症诊断和治疗中的应用，主要采用监督学习方法，并指出未来需整合复杂数据以提高准确性和效果。

Abstract

Autism spectrum disorder (ASD) is a developmental disability characterized by challenges in social interaction and communication. As the causes of ASD remain unclear, identifying relevant features and hidden correlations is crucial for early diagnosis. This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning (ML) techniques to ASD. The primary objective is to examine recent ML applications in ASD research, identifying trends, techniques, and datasets that enhance diagnosis and treatment. Supervised learning methods dominate, as they align well with ASD diagnostic needs; however, the role of deep learning is expanding with greater data availability. Emerging techniques based on hybrid methods, where unsupervised, deep learning, and fuzzy logic could be included, will be interesting to observe in the future. The review highlights key challenges and opportunities, particularly the need for models that can integrate complex data -such as genetic and clinical information- to improve diagnostic accuracy and treatment outcomes. Additionally, incorporating innovative data sources, like wearable devices and biometric sensors, could enable continuous and non-intrusive monitoring, providing a more holistic understanding of ASD. Findings emphasize that addressing current challenges requires interdisciplinary collaboration and expanded datasets tailored to ASD. Future ML models will benefit from broader multimodal data integration, enabling researchers to more comprehensively address the complexities of ASD.

Problem

• Autism Spectrum Disorder
• Machine Learning
• Diagnosis
• Treatment
• Data Integration

Innovation

• supervised learning
• deep learning
• hybrid methods
• multimodal data integration
• wearable devices