Alsbakhi Abdulhamid, Thabtah Fadi, Lu Joan
School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK.
Abu Dhabi School of Management, Abu Dhabi P.O. Box 6844, United Arab Emirates.
Bioengineering (Basel). 2025 Feb 7;12(2):160. doi: 10.3390/bioengineering12020160.
Autism Spectrum Disorder (ASD) presents challenges in early screening due to its varied nature and sophisticated early signs. From a machine-learning (ML) perspective, the primary challenges include the need for large, diverse datasets, managing the variability in ASD symptoms, providing easy-to-understand models, and ensuring ASD predictive models that can be employed across different populations. Interpretable or explainable classification algorithms, like rule-based or decision tree, play a crucial role in dealing with some of these issues by offering classification models that can be exploited by clinicians. These models offer transparency in decision-making, allowing clinicians to understand reasons behind diagnostic decisions, which is critical for trust and adoption in medical settings. In addition, interpretable classification algorithms facilitate the identification of important behavioural features and patterns associated with ASD, enabling more accurate and explainable diagnoses. However, there is a scarcity of review papers focusing on interpretable classifiers for ASD detection from a behavioural perspective. Thereby this research aimed to conduct a recent review on rule-based classification research works in order to provide added value by consolidating current research, identifying gaps, and guiding future studies. Our research would enhance the understanding of these techniques, based on data used to generate models and obtain performance by trying to highlight early detection and intervention ways for ASD. Integrating advanced AI methods like deep learning with rule-based classifiers can improve model interpretability, exploration, and accuracy in ASD-detection applications. While this hybrid approach has feature selection relevant features that can be detected in an efficient manner, rule-based classifiers can provide clinicians with transparent explanations for model decisions. This hybrid approach is critical in clinical applications like ASD, where model content is as crucial as achieving high classification accuracy.
自闭症谱系障碍（ASD）因其性质多样且早期症状复杂，在早期筛查中面临挑战。从机器学习（ML）的角度来看，主要挑战包括需要大量、多样的数据集，应对ASD症状的变异性，提供易于理解的模型，以及确保可在不同人群中应用的ASD预测模型。可解释的分类算法，如基于规则的算法或决策树，通过提供临床医生可利用的分类模型，在处理其中一些问题方面发挥着关键作用。这些模型在决策过程中具有透明度，使临床医生能够理解诊断决策背后的原因，这对于在医疗环境中获得信任和采用至关重要。此外，可解释的分类算法有助于识别与ASD相关的重要行为特征和模式，从而实现更准确且可解释的诊断。然而，从行为角度专注于用于ASD检测的可解释分类器的综述论文却很匮乏。因此，本研究旨在对基于规则的分类研究工作进行最新综述，以便通过整合当前研究、识别差距并指导未来研究来提供附加价值。我们的研究将基于用于生成模型的数据以及通过尝试突出ASD的早期检测和干预方法来获得性能，从而增强对这些技术的理解。将深度学习等先进人工智能方法与基于规则的分类器相结合，可以提高ASD检测应用中模型的可解释性、探索性和准确性。虽然这种混合方法具有能够高效检测相关特征的特征选择功能，但基于规则的分类器可以为临床医生提供模型决策的透明解释。这种混合方法在ASD等临床应用中至关重要，因为在这些应用中，模型内容与实现高分类准确率同样关键。
