Objective:

Our aim was to develop a machine learning algorithm based only on non-invasively clinic collectable predictors, for the accurate diagnosis of these disorders.

Methods:

This is an ongoing prospective cohort study (ClinicalTrials.gov identifier NCT number NCT04448340) of 78 PDD and 62 DLB subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. We used predictors such as clinico-demographic characteristics, 6 neuropsychological tests (mini mental, PD Cognitive Rating Scale, Brief Visuospatial Memory test, Symbol digit written, Wechsler adult intelligence scale, trail making A and B). We investigated logistic regression, K-Nearest Neighbors (K-NNs) Support Vector Machine (SVM), Naïve Bayes classifier, and Ensemble Model for their ability to predict successfully PDD or DLB diagnosis.

Results:

The K-NN classification model had an accuracy 91.2% of overall cases based on 15 best clinical and cognitive scores achieving 96.42% sensitivity and 81% specificity on discriminating between DLB and PDD. The binomial logistic regression classification model achieved an accuracy of 87.5% based on 15 best features, showing 93.93% sensitivity and 87% specificity. The SVM classification model had an accuracy 84.6% of overall cases based on 15 best features achieving 90.62% sensitivity and 78.58% specificity. A model created on Naïve Bayes classification had 82.05% accuracy, 93.10% sensitivity and 74.41% specificity. Finally, an Ensemble model, synthesized by the individual ones, achieved 89.74% accuracy, 93.75% sensitivity and 85.73% specificity.

Conclusion:

Machine learning method predicted with high accuracy, sensitivity and specificity PDD or DLB diagnosis based on non-invasively and easily in-the-clinic and neuropsychological tests.

中文翻译：

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一种新的机器学习算法基于临床和神经心理学评分预测路易体痴呆与帕金森病痴呆

客观的：

我们的目标是开发一种仅基于非侵入性临床可收集预测因子的机器学习算法，以准确诊断这些疾病。

方法：

这是一项正在进行的前瞻性队列研究（ClinicalTrials.gov 标识符 NCT 编号 NCT04448340），对 78 名 PDD 和 62 名 DLB 受试者进行了基线评估后至少 3 年的诊断随访。我们使用了临床人口学特征、6 项神经心理学测试（迷你心理、PD 认知评定量表、简要视觉空间记忆测试、符号数字书写、韦克斯勒成人智力量表、线索制作 A 和 B）等预测指标。我们研究了逻辑回归、K-最近邻 (K-NNs) 支持向量机 (SVM)、朴素贝叶斯分类器和集成模型成功预测 PDD 或 DLB 诊断的能力。

结果：

基于 15 个最佳临床和认知评分，K-NN 分类模型在总体病例中的准确率为 91.2%，在区分 DLB 和 PDD 方面实现了 96.42% 的敏感性和 81% 的特异性。二项式逻辑回归分类模型基于 15 个最佳特征实现了 87.5% 的准确率，显示出 93.93% 的敏感性和 87% 的特异性。基于 15 个最佳特征，SVM 分类模型的准确率为 84.6%，灵敏度为 90.62%，特异性为 78.58%。基于朴素贝叶斯分类创建的模型具有 82.05% 的准确度、93.10% 的敏感性和 74.41% 的特异性。最后，由单个模型合成的 Ensemble 模型达到了 89.74% 的准确率、93.75% 的灵敏度和 85.73% 的特异性。

结论：

机器学习方法基于非侵入性且易于临床和神经心理学测试，以高精度、灵敏度和特异性预测 PDD 或 DLB 诊断。