The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.
循环神经网络结构极大地优化了时间序列数据的处理能力，但是其网络梯度爆炸以及特征提取能力较差等问题，影响了它在轻度认知障碍（MCI）自动诊断中的应用。针对这一问题，本文提出贝叶斯优化双向长短时神经网络（BO-BiLSTM）构建MCI诊断模型的研究思路。诊断模型基于贝叶斯算法，结合先验分布与后验概率结果共同作用寻优BO-BiLSTM网络超参数，并采用功率谱密度、模糊熵以及多重分形谱等能够充分反映MCI脑认知状态的多角度特征量作为诊断模型的输入，实现MCI自动诊断。结果表明：基于特征融合的贝叶斯优化BiLSTM网络模型，MCI诊断正确率可达到98.64%，能够有效地完成MCI的诊断评估。综上，基于此优化的长短时神经网络模型，实现了MCI的自动诊断评估，为MCI智能诊断提供了一种新的模型。.
Keywords: Bayesian optimization; Bidirectional long short-term memory network; Mild cognitive impairment; Multi-feature fusion.