Department of Computer & Software Engineering, CEME, NUST, Islamabad 44800, Pakistan.
Department of Computer Engineering, HITEC University, Taxila 47080, Pakistan.
Sensors (Basel). 2022 Feb 14;22(4):1475. doi: 10.3390/s22041475.
The early prediction of Alzheimer's disease (AD) can be vital for the endurance of patients and establishes as an accommodating and facilitative factor for specialists. The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD. Neuropsychological measures (NM) and magnetic resonance imaging (MRI) biomarkers are deduced and passed on to a recurrent neural network (RNN). In the RNN, we have used long short-term memory (LSTM), and the proposed model will predict the biomarkers (feature vectors) of patients after 6, 12, 21 18, 24, and 36 months. These predicted biomarkers will go through fully connected neural network layers. The NN layers will then predict whether these RNN-predicted biomarkers belong to an AD patient or a patient with a mild cognitive impairment (MCI). The developed methodology has been tried on an openly available informational dataset (ADNI) and accomplished an accuracy of 88.24%, which is superior to the next-best available algorithms.
阿尔茨海默病（AD）的早期预测对于患者的预后至关重要，并为专家提供了一种适应性和促进性的因素。本研究提出了一种基于机器学习（ML）方法的 AD 预测的机器人预测结构。神经心理学测量（NM）和磁共振成像（MRI）生物标志物被推导出来并传递给一个递归神经网络（RNN）。在 RNN 中，我们使用了长短时记忆（LSTM），提出的模型将预测患者在 6、12、21、18、24 和 36 个月后的生物标志物（特征向量）。这些预测的生物标志物将经过全连接神经网络层。然后，NN 层将预测这些 RNN 预测的生物标志物是否属于 AD 患者或轻度认知障碍（MCI）患者。所开发的方法已在公开可用的信息数据集（ADNI）上进行了尝试，并达到了 88.24%的准确率，优于下一个最佳可用算法。
