Department of Computer Science & Engineering, Delhi Technological University, New Delhi, India.
Department of Computer Science & Engineering, Delhi Technological University, New Delhi, India.
J Neurosci Methods. 2025 Jan;413:110319. doi: 10.1016/j.jneumeth.2024.110319. Epub 2024 Nov 8.
Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition marked by challenges in social communication, sensory processing, and behavioral regulation. The delayed diagnosis of ASD significantly impedes timely interventions, which can exacerbate symptom severity. With approximately 62 million individuals affected worldwide, the demand for efficient diagnostic tools is critical. This study introduces a novel framework that combines a White Shark Optimization (WSO)-based feature selection method with a Bidirectional Long Short-Term Memory (Bi-LSTM) classifier for enhanced autism classification. Utilizing the WSO technique, we identify key features from autism screening datasets, which markedly improves the model's predictive capabilities. The optimized feature set is then processed by the Bi-LSTM classifier, enhancing its efficiency in handling sequential data. We comprehensively address methodological challenges, including overfitting, generalization, interpretability, and computational efficiency. Furthermore, we conduct a comparative analysis against baseline algorithms such as Neural Networks, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, while also employing Particle Swarm Optimization (PSO) for feature selection validation. We evaluate performance metrics, including accuracy, F1-score, specificity, precision, and sensitivity across three ASD datasets: Toddlers, Adults, and Children. Our results demonstrate that the WS-BiTM model significantly outperforms baseline methods, achieving accuracies of 97.6 %, 96.2 %, and 96.4 % on the respective datasets. Additionally, we implemented leave-one-dataset cross-validation and confirmed the statistical significance of our findings through a paired t-test, supplemented by an ablation study to detail the contributions of individual model components. These findings highlight the potential of the WS-BiTM model as a robust tool for ASD classification.
自闭症谱系障碍（ASD）是一种多方面的神经发育障碍，其特点是在社交沟通、感觉处理和行为调节方面存在挑战。ASD 的延迟诊断严重阻碍了及时的干预，从而加剧了症状的严重程度。全球大约有 6200 万人受到影响，因此对高效诊断工具的需求至关重要。本研究提出了一种新的框架，该框架将基于白鲨优化（WSO）的特征选择方法与双向长短期记忆（Bi-LSTM）分类器相结合，以增强自闭症分类。我们利用 WSO 技术从自闭症筛查数据集中识别关键特征，从而显著提高了模型的预测能力。然后，优化后的特征集由 Bi-LSTM 分类器处理，提高了其处理序列数据的效率。我们全面解决了方法学上的挑战，包括过拟合、泛化、可解释性和计算效率。此外，我们还与基线算法（如神经网络、卷积神经网络（CNN）和长短期记忆（LSTM）网络）进行了对比分析，同时还使用粒子群优化（PSO）进行了特征选择验证。我们在三个自闭症数据集（幼儿、成人和儿童）上评估了性能指标，包括准确性、F1 分数、特异性、精度和敏感性。我们的结果表明，WS-BiTM 模型显著优于基线方法，在各自的数据集上的准确率分别达到 97.6%、96.2%和 96.4%。此外，我们还实施了留一数据集交叉验证，并通过配对 t 检验证实了我们的发现具有统计学意义，同时还进行了消融研究以详细说明各个模型组件的贡献。这些发现突出了 WS-BiTM 模型作为自闭症分类的强大工具的潜力。
