College of Computer Science, King Khalid University, Abha, Saudi Arabia.
Government Pharmacy College, Sikkim, India.
Technol Health Care. 2024;32(3):1991-2007. doi: 10.3233/THC-231807.
Coronary heart disease (CHD) is one of the deadliest diseases and a risk prediction model for cardiovascular conditions is needed. Due to the huge number of features that lead to heart problems, it is often difficult for an expert to evaluate these huge features into account. So, there is a need of appropriate feature selection for the given CHD dataset. For early CHD detection, deep learning modes (DL) show promising results in the existing studies.
This study aimed to develop a deep convolution neural network (CNN) model for classification with a selected number of efficient features using the LASSO (least absolute shrinkage and selection operator) technique. Also, aims to compare the model with similar studies and analyze the performance of the proposed model using accuracy measures.
The CHD dataset of NHANES (National Health and Nutritional Examination Survey) was examined with 49 features using LASSO technique. This research work is an attempt to apply an improved CNN model for the classification of the CHD dataset with huge features CNN model with feature extractor consists of a fully connected layer with two convolution 1D layers, and classifier part consists of two fully connected layers with SoftMax function was trained on this dataset. Metrics like accuracy recall, specificity, and ROC were used for the evaluation of the proposed model.
The feature selection was performed by applying the LASSO model. The proposed CNN model achieved 99.36% accuracy, while previous studies model achieved over 80 to 92% accuracy.
The application of the proposed CNN with the LASSO model for the classification of CHD can speed up the diagnosis of CHD and appears to be effective in predicting cardiovascular disease based on risk features.
冠心病（CHD）是最致命的疾病之一，因此需要建立心血管疾病风险预测模型。由于导致心脏问题的特征数量巨大，专家通常难以将这些大量特征纳入考虑。因此，需要对给定的 CHD 数据集进行适当的特征选择。在早期 CHD 检测中，深度学习模式（DL）在现有研究中显示出有前途的结果。
本研究旨在开发一种深度卷积神经网络（CNN）模型，使用 LASSO（最小绝对收缩和选择算子）技术从选定数量的有效特征中进行分类。此外，旨在与类似的研究进行比较，并使用精度度量来分析所提出模型的性能。
使用 LASSO 技术检查 NHANES（国家健康和营养检查调查）的 CHD 数据集，该数据集包含 49 个特征。这项研究工作旨在尝试应用改进的 CNN 模型，对具有大量特征的 CHD 数据集进行分类。CNN 模型的特征提取器由一个全连接层和两个一维卷积层组成，分类器部分由两个全连接层和 SoftMax 函数组成。该数据集上训练了该模型。使用准确性、召回率、特异性和 ROC 等指标来评估所提出的模型。
通过应用 LASSO 模型进行特征选择。所提出的 CNN 模型实现了 99.36%的准确率，而以前的研究模型实现了 80%至 92%的准确率。
应用带 LASSO 模型的 CNN 对 CHD 进行分类可以加快 CHD 的诊断速度，并且似乎可以基于风险特征有效地预测心血管疾病。
