Department of Computer Science and Engineering, National Institute of Technology Calicut, Kozhikode, 673601, Kerala, India.
Comput Biol Med. 2024 Sep;179:108909. doi: 10.1016/j.compbiomed.2024.108909. Epub 2024 Jul 24.
Attention deficit hyperactivity disorder (ADHD) is a heterogeneous neurobehavioral disorder that is common in children and adolescents. Inattention, impulsivity, and hyperactivity are the key symptoms of ADHD patients. Traditional clinical assessments delay ADHD diagnosis and increase undiagnosed cases and costs, as well. The use of deep learning (DL) and machine learning (ML)-based objective techniques for diagnosing ADHD has grown exponentially in recent years as the efficiency of early diagnosis has improved. This research highlights the significance of utilizing feature selection techniques before constructing machine learning models on activity datasets. It also explores the distinctions between specific time-interval activity data and broader interval activity data in identifying ADHD patients from the clinical control group. Five ML models were developed and tested to assess the performance of two sets of features and different categories of activity data in predicting ADHD. The study concludes with the following findings: (i) the model's performance showed a notable improvement of 0.11 in accuracy with the adoption of a precise feature selection process; (ii) activity data recorded in the morning and at night are more significant predictors of ADHD compared to other times; (iii) the utilization of specific time interval data is crucial for ADHD prediction; (iv) the random forest performs better than the other machine learning models used in the study, with 84% accuracy, 79% precision, 85% F1-score, and 92% recall. As we move into an era where early disease prediction is possible, combining artificial intelligence methods with activity data could create a strong framework for helping doctors make decisions that can be used far beyond hospitals.
注意缺陷多动障碍（ADHD）是一种异质性神经行为障碍，在儿童和青少年中很常见。注意力不集中、冲动和多动是 ADHD 患者的主要症状。传统的临床评估会延迟 ADHD 的诊断，并增加未确诊的病例和成本。近年来，深度学习（DL）和基于机器学习（ML）的客观技术在 ADHD 诊断中的应用呈指数级增长，因为早期诊断的效率得到了提高。本研究强调了在构建基于活动数据集的机器学习模型之前，使用特征选择技术的重要性。它还探讨了特定时间间隔活动数据和更广泛间隔活动数据在从临床对照组中识别 ADHD 患者方面的区别。开发和测试了五个 ML 模型，以评估两组特征和不同类别的活动数据在预测 ADHD 方面的性能。研究得出以下结论：（i）采用精确的特征选择过程后，模型的性能在准确性方面提高了 0.11；（ii）与其他时间相比，早晨和晚上记录的活动数据是预测 ADHD 的更重要指标；（iii）特定时间间隔数据的利用对于 ADHD 预测至关重要；（iv）随机森林的性能优于研究中使用的其他机器学习模型，准确率为 84%，精度为 79%，F1 得分为 85%，召回率为 92%。随着我们进入早期疾病预测成为可能的时代，将人工智能方法与活动数据相结合可能会为帮助医生做出决策创建一个强大的框架，这些决策可以在医院之外得到广泛应用。
