Medical Equipment Department, Changzhou No2 Hospital Nanjing Medical University, Changzhou, 213164, Jiangsu, China.
Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, 13713, Diriyah, Riyadh, Saudi Arabia.
Sci Rep. 2024 Sep 18;14(1):21777. doi: 10.1038/s41598-024-71932-z.
To identify patterns in big medical datasets and use Deep Learning and Machine Learning (ML) to reliably diagnose Cardio Vascular Disease (CVD), researchers are currently delving deeply into these fields. Training on large datasets and producing highly accurate validation results is exceedingly difficult. Furthermore, early and precise diagnosis is necessary due to the increased global prevalence of cardiovascular disease (CVD). However, the increasing complexity of healthcare datasets makes it challenging to detect feature connections and produce precise predictions. To address these issues, the Intelligent Cardiovascular Disease Diagnosis based on Ant Colony Optimisation with Enhanced Deep Learning (ICVD-ACOEDL) model was developed. This model employs feature selection (FS) and hyperparameter optimization to diagnose CVD. Applying a min-max scaler, medical data is first consistently prepared. The key feature that sets ICVD-ACOEDL apart is the use of Ant Colony Optimisation (ACO) to select an optimal feature subset, which in turn helps to upgrade the performance of the ensuring deep learning enhanced neural network (DLENN) classifier. The model reforms the hyperparameters of DLENN for CVD classification using Bayesian optimization. Comprehensive evaluations on benchmark medical datasets show that ICVD-ACOEDL exceeds existing techniques, indicating that it could have a significant impact on CVD diagnosis. The model furnishes a workable way to increase CVD classification efficiency and accuracy in real-world medical situations by incorporating ACO for feature selection, min-max scaling for data pre-processing, and Bayesian optimization for hyperparameter tweaking.
为了在大型医疗数据集识别模式并利用深度学习和机器学习（ML）可靠地诊断心血管疾病（CVD），研究人员目前正在深入研究这些领域。在大型数据集上进行训练并产生高度准确的验证结果非常困难。此外，由于心血管疾病（CVD）在全球的患病率不断增加，早期和精确的诊断是必要的。然而，由于医疗数据集的日益复杂，检测特征连接和生成精确预测变得具有挑战性。为了解决这些问题，开发了基于蚁群优化增强深度学习的智能心血管疾病诊断（ICVD-ACOEDL）模型。该模型采用特征选择（FS）和超参数优化来诊断 CVD。应用 min-max 缩放器，首先一致地准备医疗数据。ICVD-ACOEDL 的关键特点是使用蚁群优化（ACO）选择最佳特征子集，这反过来有助于提升确保深度学习增强神经网络（DLENN）分类器的性能。该模型使用贝叶斯优化来调整用于 CVD 分类的 DLENN 的超参数。在基准医疗数据集上的综合评估表明，ICVD-ACOEDL 优于现有技术，这表明它可能对 CVD 诊断产生重大影响。该模型通过使用 ACO 进行特征选择、min-max 缩放进行数据预处理以及贝叶斯优化进行超参数调整，为在实际医疗情况下提高 CVD 分类效率和准确性提供了一种可行的方法。
