Background

Alzheimer's disease can be diagnosed through various clinical methods. Among them, electroencephalography has proven to be a powerful, non-invasive, affordable, and painless tool for its diagnosis.

Objectives

In this study, eight machine learning (ML) approaches, including SVM, BLDA, DT, GNB, KNN, RF, and deep learning (DL) methods such as RNN and RBF, were employed to classify Alzheimer's disease into two stages: moderate Alzheimer's disease (ADM) and advanced Alzheimer's disease (ADA).

Material and methods

To this aim, electroencephalography data collected from five different hospitals over a decade has been used. A novel method based on neural networks has been proposed to increase accuracy and obtain fast classification times.

Results

Results show that deep neuronal networks based on radial basis functions initialized with fuzzy means achieved the best balanced accuracy with 96.66% accuracy in ADA classification and 93.31% accuracy in ADM classification.

Conclusion

Apart from improving accuracy, it is noteworthy that this algorithm had never been used before to classify patients with Alzheimer's disease.

中文翻译：

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使用模糊逻辑初始化的基于径向基函数的神经网络对中度和晚期阿尔茨海默病患者进行分类

背景

阿尔茨海默病可以通过多种临床方法进行诊断。其中，脑电图已被证明是一种强大的、非侵入性的、负担得起的、无痛的诊断工具。

目标

在这项研究中，采用了八种机器学习（ML）方法，包括SVM 、BLDA 、DT、GNB、KNN、RF以及RNN和RBF等深度学习（DL）方法，将阿尔茨海默病分为两个阶段：中度阿尔茨海默病疾病（ADM）和晚期阿尔茨海默病（ADA）。

材料与方法

为此，使用了十多年来从五家不同医院收集的脑电图数据。提出了一种基于神经网络的新方法来提高准确性并获得快速分类时间。

结果

结果表明，基于模糊均值初始化的径向基函数的深度神经网络取得了最佳的平衡精度，ADA分类准确率为96.66%，ADM分类准确率为93.31%。

结论

除了提高准确性之外，值得注意的是，该算法以前从未用于对阿尔茨海默病患者进行分类。