Simfukwe Chanda, A An Seong Soo, Youn Young Chul
Department of Bionano Technology, Gachon University, Seongnam-si, South Korea.
Department of Neurology, College of Medicine, Chung-Ang University Seoul, Seoul, South Korea.
Appl Neuropsychol Adult. 2024 Sep 9:1-12. doi: 10.1080/23279095.2024.2392282.
Neuropsychological tests (NPTs) are standard tools for assessing cognitive function. These tools can evaluate the cognitive status of a subject, which can be time-consuming and expensive for interpretation. Therefore, this paper aimed to optimize the systematic NPTs by machine learning and develop new classification models for differentiating healthy controls (HC), mild cognitive impairment, and Alzheimer's disease dementia (ADD) among groups of subjects.
A total dataset of 14,926 subjects was obtained from the formal 46 NPTs based on the Seoul Neuropsychological Screening Battery (SNSB). The statistical values of the dataset included an age of 70.18 ± 7.13 with an education level of 8.18 ± 5.50 and a diagnosis group of three; HC, MCI, and ADD. The dataset was preprocessed and classified in two- and three-way machine-learning classification from scikit-learn (www.scikit-learn.org) to differentiate between HC versus MCI, HC versus ADD, HC versus Cognitive Impairment (CI) (MCI + ADD), and HC versus MCI versus ADD. We compared the performance of seven machine learning algorithms, including Naïve Bayes (NB), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), AdaBoost, and linear discriminant analysis (LDA). The accuracy, sensitivity, specificity, positive predicted value (PPV), negative predictive value (NPV), area under the curve (AUC), confusion matrixes, and receiver operating characteristic (ROC) were obtained from each model based on the test dataset.
The trained models based on 29 best-selected NPT features were evaluated, the model with the RF algorithm yielded the best accuracy, sensitivity, specificity, PPV, NPV, and AUC in all four models: HC versus MCI was 98%, 98%, 97%, 98%, 97%, and 99%; HC versus ADD was 98%, 99%, 96%, 97%, 98%, and 99%; HC versus CI was 97%, 99%, 92%, 97%, 97%, and 99% and HC versus MCI versus ADD was 97%, 96%, 98%, 97%, 98%, and 99%, respectively, in predicting of cognitive impairment among subjects.
According to the results, the RF algorithm was the best classification model for both two- and three-way classification among the seven algorithms trained on an imbalanced NPTs SNSB dataset. The trained models proved useful for diagnosing MCI and ADD in patients with normal NPTs. These models can optimize cognitive evaluation, enhance diagnostic accuracy, and reduce missed diagnoses.
神经心理学测试（NPTs）是评估认知功能的标准工具。这些工具可以评估受试者的认知状态，但其解读可能既耗时又昂贵。因此，本文旨在通过机器学习优化系统性NPTs，并开发新的分类模型以区分健康对照（HC）、轻度认知障碍和阿尔茨海默病性痴呆（ADD）患者群体。
基于首尔神经心理学筛查量表（SNSB），从正式的46项NPTs中获得了一个包含14926名受试者的总数据集。该数据集的统计值包括年龄70.18±7.13、教育水平8.18±5.50以及三个诊断组：HC、MCI和ADD。对该数据集进行预处理，并使用来自scikit-learn（www.scikit-learn.org）的二分类和三分类机器学习分类方法进行分类，以区分HC与MCI、HC与ADD、HC与认知障碍（CI）（MCI + ADD）以及HC与MCI与ADD。我们比较了七种机器学习算法的性能，包括朴素贝叶斯（NB）、随机森林（RF）、决策树（DT）、k近邻（KNN）、支持向量机（SVM）、AdaBoost和线性判别分析（LDA）。基于测试数据集，从每个模型中获得准确率、敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）、曲线下面积（AUC）、混淆矩阵和受试者工作特征（ROC）。
对基于29个最佳选择的NPT特征训练的模型进行了评估，在所有四个模型中，采用RF算法的模型在准确率、敏感性、特异性、PPV、NPV和AUC方面表现最佳：在预测受试者认知障碍方面，HC与MCI分别为98%、98%、97%、98%、97%和99%；HC与ADD分别为98%、99%、96%、97%、98%和99%；HC与CI分别为97%、99%、92%、97%、97%和99%；HC与MCI与ADD分别为97%、96%、98%、97%、98%和99%。
根据结果，在基于不平衡的NPTs SNSB数据集训练的七种算法中，RF算法是二分类和三分类的最佳分类模型。训练后的模型被证明对诊断NPTs正常患者的MCI和ADD有用。这些模型可以优化认知评估，提高诊断准确性，并减少漏诊。
