Zheng Huilin, Sherazi Syed Waseem Abbas, Lee Jong Yun
Department of Computer Science, Chungbuk National University, Cheongju, Republic of Korea.
College of Computer Science and Engineering, Guilin University of Technology, Guilin, China.
Front Cardiovasc Med. 2024 Mar 19;11:1276608. doi: 10.3389/fcvm.2024.1276608. eCollection 2024.
Hypertension is one of the most serious risk factors and the leading cause of mortality in patients with cardiovascular diseases (CVDs). It is necessary to accurately predict the mortality of patients suffering from CVDs with hypertension. Therefore, this paper proposes a novel cost-sensitive deep neural network (CSDNN)-based mortality prediction model for out-of-hospital acute myocardial infarction (AMI) patients with hypertension on imbalanced data.
The synopsis of our research is as follows. First, the experimental data is extracted from the Korea Acute Myocardial Infarction Registry-National Institutes of Health (KAMIR-NIH) and preprocessed with several approaches. Then the imbalanced experimental dataset is divided into training data (80%) and test data (20%). After that, we design the proposed CSDNN-based mortality prediction model, which can solve the skewed class distribution between the majority and minority classes in the training data. The threshold moving technique is also employed to enhance the performance of the proposed model. Finally, we evaluate the performance of the proposed model using the test data and compare it with other commonly used machine learning (ML) and data sampling-based ensemble models. Moreover, the hyperparameters of all models are optimized through random search strategies with a 5-fold cross-validation approach.
In the result, the proposed CSDNN model with the threshold moving technique yielded the best results on imbalanced data. Additionally, our proposed model outperformed the best ML model and the classic data sampling-based ensemble model with an AUC of 2.58% and 2.55% improvement, respectively. It aids in decision-making and offers a precise mortality prediction for AMI patients with hypertension.
高血压是心血管疾病（CVD）患者最严重的危险因素之一，也是导致死亡的主要原因。准确预测高血压CVD患者的死亡率很有必要。因此，本文针对数据不均衡情况下院外急性心肌梗死（AMI）合并高血压患者，提出了一种基于成本敏感深度神经网络（CSDNN）的死亡率预测模型。
我们的研究概要如下。首先，从韩国急性心肌梗死登记处 - 国立卫生研究院（KAMIR - NIH）提取实验数据，并采用多种方法进行预处理。然后将不均衡的实验数据集分为训练数据（80%）和测试数据（20%）。之后，我们设计了所提出的基于CSDNN的死亡率预测模型，该模型可以解决训练数据中多数类和少数类之间的类分布不均衡问题。还采用了阈值移动技术来提高所提模型的性能。最后，我们使用测试数据评估所提模型的性能，并将其与其他常用的机器学习（ML）模型和基于数据采样的集成模型进行比较。此外，通过随机搜索策略和5折交叉验证方法对所有模型的超参数进行优化。
结果表明，所提出的采用阈值移动技术的CSDNN模型在不均衡数据上取得了最佳效果。此外，我们提出的模型分别比最佳ML模型和基于经典数据采样的集成模型性能提高了2.58%和2.55%，其AUC更高。它有助于决策制定，并为高血压AMI患者提供精确的死亡率预测。
