Alutaibi Ahmed Ibrahim, Sharma Sunil Kumar, Khan Ahmad Raza
Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia.
Department of Information Systems, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia.
Comput Biol Med. 2025 May;190:110038. doi: 10.1016/j.compbiomed.2025.110038. Epub 2025 Mar 21.
Autism Spectrum Disorder (ASD) is a complex neurological condition that impairs the ability to interact, communicate, and behave. It is becoming increasingly prevalent worldwide, with an increase in the number of young children diagnosed with ASD in Saudi Arabia. Timely identification and customized interventions are essential for enhancing developmental outcomes. However, existing diagnostic approaches are subjective, limiting the cost-effectiveness of their utilization and the uniformity of their outcomes across different communities. In light of these concerns, this study presents a two-phase deep learning framework for autism detection with lifestyle advice using reinforcement learning. In the first phase, the proposed framework utilizes advanced multiscale statistical techniques for feature extraction, such as measures of central tendencies, variability indices, and percentiles, incorporated with the CosmoNest Optimizer, which is a hybrid of the African Vultures Optimization Algorithm and Butterfly Optimization Algorithm. For accurate ASD identification, these optimized features were classified using Capsule DenseNet++, an advanced deep learning model that increases feature representation efficiency and interpretability. In the second stage, we implement a personalized lifestyle recommendation system using the Proximal Policy Optimization (PPO) algorithm, a reinforcement learning algorithm. In the PPO approach, lifestyle decisions are sequential actions aimed at optimizing interventions, therapies, or daily activities for a given person. The PPO system dynamically learns and adapts recommendations over time to improve its effectiveness. The framework was developed in Python and tested on two datasets: autism screening data and ASD screening data for toddlers in Saudi Arabia. The performance of the detection model was recorded in terms of accuracy (99.2 % and 99.3 %, respectively), precision (98.5 % and 98.7 %, respectively), sensitivity (98.7 % and 98.9 %, respectively), and F1-score (99.1 % and 99.2 %, respectively), demonstrating its robustness for ASD detection across both datasets.
自闭症谱系障碍（ASD）是一种复杂的神经疾病，会损害互动、沟通和行为能力。它在全球范围内日益普遍，沙特阿拉伯被诊断为ASD的幼儿数量也在增加。及时识别和定制干预措施对于改善发育结果至关重要。然而，现有的诊断方法具有主观性，限制了其使用的成本效益以及不同社区结果的一致性。鉴于这些问题，本研究提出了一个两阶段的深度学习框架，用于通过强化学习进行自闭症检测并提供生活方式建议。在第一阶段，所提出的框架利用先进的多尺度统计技术进行特征提取，例如集中趋势度量、变异指数和百分位数，并结合了CosmoNest优化器，它是非洲秃鹫优化算法和蝴蝶优化算法的混合体。为了准确识别ASD，这些优化后的特征使用Capsule DenseNet++进行分类，这是一种先进的深度学习模型，可提高特征表示效率和可解释性。在第二阶段，我们使用近端策略优化（PPO）算法（一种强化学习算法）实现了个性化生活方式推荐系统。在PPO方法中，生活方式决策是一系列旨在为特定人员优化干预措施、治疗方法或日常活动的行动。PPO系统会随着时间动态学习并调整建议，以提高其有效性。该框架是用Python开发的，并在两个数据集上进行了测试：自闭症筛查数据和沙特阿拉伯幼儿的ASD筛查数据。检测模型的性能记录为准确率（分别为99.2%和99.3%）、精确率（分别为98.5%和98.7%）、灵敏度（分别为98.7%和98.9%）以及F1分数（分别为99.1%和99.2%），表明其在两个数据集上进行ASD检测的稳健性。
