Despite the popularity of the continuous performance test (CPT) in the diagnosis of attention-deficit/hyperactivity disorder (ADHD), its specificity, sensitivity, and ecological validity are still debated. To address some of the known shortcomings of traditional analysis and interpretation of CPT data, the present study applied a machine learning-based model (ML) using CPT indices for the Prediction of ADHD.Using a retrospective factorial fitting, followed by a bootstrap technique, we trained, cross-validated, and tested learning models on CPT performance data of 458 children aged 6–12 years (213 children with ADHD and 245 typically developed children). We used the MOXO-CPT version that included visual and auditory stimuli distractors. Results showed that the ML proposed model performed better and had a higher accuracy than the benchmark approach that used clinical data only. Using the CPT total score (that included all four indices: Attention, Timeliness, Hyperactivity, and Impulsiveness), as well as four control variables [age, gender, day of the week (DoW), time of day (ToD)], provided the most salient information for discriminating children with ADHD from their typically developed peers. This model had an accuracy rate of 87%, a sensitivity rate of 89%, and a specificity rate of 84%. This performance was 34% higher than the best-achieved accuracy of the benchmark model. The ML detection model could classify children with ADHD with high accuracy based on CPT performance. ML model of ADHD holds the promise of enhancing, perhaps complementing, behavioral assessment and may be used as a supportive measure in the evaluation of ADHD.

中文翻译：

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使用 CPT 数据的基于机器的 ADHD 预测模型

尽管连续表现测试 (CPT) 在注意力缺陷/多动障碍 (ADHD) 的诊断中很受欢迎，但其特异性、敏感性和生态有效性仍存在争议。为了解决 CPT 数据传统分析和解释的一些已知缺点，本研究应用了基于机器学习的模型 (ML)，使用 CPT 指数预测 ADHD。使用回顾性因子拟合，然后是引导技术，我们在 458 名 6-12 岁儿童（213 名患有多动症的儿童和 245 名正常发育的儿童）的 CPT 表现数据上训练、交叉验证和测试了学习模型。我们使用了包含视觉和听觉刺激干扰物的 MOXO-CPT 版本。结果表明，与仅使用临床数据的基准方法相比，ML 提出的模型表现更好，准确性更高。使用 CPT 总分（包括所有四个指标：注意力、及时性、多动和冲动），以及四个控制变量 [年龄、性别、星期几 (DoW)、一天中的时间 (ToD)]，提供用于将 ADHD 儿童与其典型发育的同龄人区分开来的最重要信息。该模型的准确率为 87%，敏感率为 89%，特异率为 84%。该性能比基准模型的最佳实现精度高 34%。ML 检测模型可以根据 CPT 性能对 ADHD 儿童进行高精度分类。ADHD 的 ML 模型有望增强，也许补充，