Abstract

Objectives

Neuropsychological tests (NPTs) are widely used tools to evaluate cognitive functioning. The interpretation of these tests can be time-consuming and requires a specialized clinician. For this reason, we trained machine learning models that detect normal controls (NC), cognitive impairment (CI), and dementia among subjects.

Patients and methods

A total number of 14,927 subject datasets were collected from the formal neuropsychological assessments Seoul Neuropsychological Screening Battery (SNSB) by well-qualified neuropsychologists. The dataset included 44 NPTs of SNSB, age, education level, and diagnosis of each participant. The dataset was preprocessed and classified according to three different classes NC, CI, and dementia. We trained machine-learning with a supervised machine learning classifier algorithm support vector machine (SVM) 30 times with classification from scikit-learn (https://scikit-learn.org/stable/) to distinguish the prediction accuracy, sensitivity, and specificity of the models; NC vs. CI, NC vs. dementia, and NC vs. CI vs. dementia. Confusion matrixes were plotted using the testing dataset for each model.

Results

The trained model's 30 times mean accuracies for predicting cognitive states were as follows; NC vs. CI model was 88.61 ± 1.44%, NC vs. dementia model was 97.74 ± 5.78%, and NC vs. CI vs. dementia model was 83.85 ± 4.33%. NC vs. dementia showed the highest accuracy, sensitivity, and specificity of 97.74 ± 5.78, 97.99 ± 5.78, and 96.08 ± 4.33% in predicting dementia among subjects, respectively.

Conclusion

Based on the results, the SVM algorithm is more appropriate in training models on an imbalanced dataset for a good prediction accuracy compared to natural network and logistic regression algorithms. The NC vs. dementia machine-learning trained model with SVM based on NPTs SNSB dataset could assist neuropsychologists in classifying the cognitive function of subjects.

中文翻译：

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使用机器学习进行认知障碍筛查的神经心理学测试

摘要

目标

神经心理学测试 (NPT) 是广泛用于评估认知功能的工具。这些测试的解释可能很耗时，并且需要专业的临床医生。出于这个原因，我们训练了机器学习模型来检测受试者的正常对照 (NC)、认知障碍 (CI) 和痴呆症。

患者和方法

合格的神经心理学家从正式的神经心理学评估首尔神经心理学筛查小组 (SNSB) 中收集了总共 14,927 个主题数据集。该数据集包括 SNSB、年龄、教育水平和每个参与者的诊断的 44 个 NPT。数据集根据三个不同的类别 NC、CI 和痴呆进行预处理和分类。我们使用监督机器学习分类器算法支持向量机 (SVM) 训练机器学习 30 次，并使用 scikit-learn (https://scikit-learn.org/stable/) 进行分类，以区分预测准确性、敏感性和特异性模型；NC与CI、NC与痴呆、NC与CI与痴呆失智。使用每个模型的测试数据集绘制混淆矩阵。

结果

训练后的模型预测认知状态的 30 次平均准确率如下：NC vs. CI 模型为 88.61 ± 1.44%，NC vs.痴呆模型为 97.74 ± 5.78%，NC vs. CI vs.痴呆模型为 83.85 ± 4.33%。NC与痴呆在预测受试者痴呆方面分别显示出最高的准确性、敏感性和特异性，分别为 97.74 ± 5.78、97.99 ± 5.78 和 96.08 ± 4.33%。

结论

根据结果​​，与自然网络和逻辑回归算法相比，SVM 算法更适合在不平衡数据集上训练模型，以获得良好的预测精度。基于 NPTs SNSB 数据集的支持向量机的 NC与痴呆机器学习训练模型可以帮助神经心理学家对受试者的认知功能进行分类。