Revathi T K, Balasubramaniam Sathiyabhama, Sureshkumar Vidhushavarshini, Dhanasekaran Seshathiri
Department of Computer Science and Engineering, Sona College of Technology, Salem 636005, India.
Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai 600026, India.
Diagnostics (Basel). 2024 Jan 23;14(3):239. doi: 10.3390/diagnostics14030239.
Cardiovascular diseases, prevalent as leading health concerns, demand early diagnosis for effective risk prevention. Despite numerous diagnostic models, challenges persist in network configuration and performance degradation, impacting model accuracy. In response, this paper introduces the Optimally Configured and Improved Long Short-Term Memory (OCI-LSTM) model as a robust solution. Leveraging the Salp Swarm Algorithm, irrelevant features are systematically eliminated, and the Genetic Algorithm is employed to optimize the LSTM's network configuration. Validation metrics, including the accuracy, sensitivity, specificity, and F1 score, affirm the model's efficacy. Comparative analysis with a Deep Neural Network and Deep Belief Network establishes the OCI-LSTM's superiority, showcasing a notable accuracy increase of 97.11%. These advancements position the OCI-LSTM as a promising model for accurate and efficient early diagnosis of cardiovascular diseases. Future research could explore real-world implementation and further refinement for seamless integration into clinical practice.
心血管疾病作为主要的健康问题普遍存在，需要早期诊断以进行有效的风险预防。尽管有众多诊断模型，但在网络配置和性能退化方面仍存在挑战，影响模型准确性。作为应对措施，本文引入了最优配置与改进的长短期记忆（OCI-LSTM）模型作为一种强大的解决方案。利用鹈鹕群算法，系统地消除了无关特征，并采用遗传算法优化LSTM的网络配置。包括准确率、灵敏度、特异性和F1分数在内的验证指标证实了该模型的有效性。与深度神经网络和深度信念网络的对比分析确立了OCI-LSTM的优越性，显示准确率显著提高了97.11%。这些进展使OCI-LSTM成为用于心血管疾病准确高效早期诊断的有前景的模型。未来研究可以探索其在现实世界中的应用以及进一步优化，以便无缝融入临床实践。
