Banumathy D, Vetriselvi T, Venkatachalam K, Cho Jaehyuk
Department of Computer Science and Engineering, Paavai Engineering College, Namakkal, India.
School of Computer science and Engineering, VIT University, Vellore, India.
PeerJ Comput Sci. 2024 Dec 13;10:e2498. doi: 10.7717/peerj-cs.2498. eCollection 2024.
The early detection and accurate diagnosis of cardiovascular diseases is vital to reduce global morbidity and death rates. In this work, the quantum-inspired seagull optimization algorithm (QISOA) combined with a deep belief network (DBN) is proposed to improve the identification of cardiovascular disorders. As part of preprocessing, cleaning, transformation, and standardization are performed to eliminate noise, inconsistencies, and scaling issues in the data. QISOA is used to optimize the weights and biases of the DBN model, enhancing its prediction efficiency. The algorithm incorporates quantum mechanics concepts to develop its exploration potential further, leading to faster convergence and increased global search efficiency. Optimized DBN provides efficient acquisition of hierarchical representations of the data, which results in improved feature learning and classification accuracy. The publicly accessible Cleveland Heart Disease dataset is used to assess the performance of the suggested model. Extensive experiments are conducted to demonstrate the superior performance of the QISOA-optimized DBN model compared to traditional machine learning and other metaheuristic-based models. Initially, machine learning models such as support vector machines, decision trees, Random Forests, multi-layer perceptrons, and fully connected networks were considered for comparison with the cardiovascular predictive performance of the DBN model. Further, meta-heuristic optimization algorithms such as particle swarm optimization, genetic algorithm, grey wolf optimization, cuckoo search optimization and crow search algorithm are combined with the machine learning models and the classification efficiency is evaluated. Additionally, few state-of-the-art techniques proposed in the existing literature are investigated and compared against the proposed model. It was evident from the comprehensive performance assessment of the proposed model that it yields a higher accuracy of 98.6% with precision, recall, and F1-scores of 97.6%, 96.8%, and 97.1%, respectively, compared to other traditional and existing models for cardiovascular disease prediction.
心血管疾病的早期检测和准确诊断对于降低全球发病率和死亡率至关重要。在这项工作中，提出了结合深度信念网络（DBN）的量子启发式海鸥优化算法（QISOA），以改善心血管疾病的识别。作为预处理的一部分，进行清理、转换和标准化以消除数据中的噪声、不一致性和缩放问题。QISOA用于优化DBN模型的权重和偏差，提高其预测效率。该算法纳入量子力学概念以进一步开发其探索潜力，从而实现更快的收敛和更高的全局搜索效率。优化后的DBN能够高效获取数据的分层表示，从而提高特征学习和分类准确率。使用公开可用的克利夫兰心脏病数据集来评估所提出模型的性能。进行了广泛的实验，以证明与传统机器学习和其他基于元启发式的模型相比，QISOA优化的DBN模型具有卓越的性能。最初，考虑了支持向量机、决策树、随机森林、多层感知器和全连接网络等机器学习模型，以与DBN模型的心血管预测性能进行比较。此外，将粒子群优化、遗传算法、灰狼优化、布谷鸟搜索优化和乌鸦搜索算法等元启发式优化算法与机器学习模型相结合，并评估分类效率。此外，还研究了现有文献中提出的一些最新技术，并与所提出的模型进行比较。从所提出模型的综合性能评估中可以明显看出，与其他用于心血管疾病预测的传统和现有模型相比，它产生了更高的准确率，达到98.6%，精确率、召回率和F1分数分别为97.6%、96.8%和97.1%。
