论文标题
心脏病发作分类系统使用接受粒子群优化训练的神经网络
Heart Attack Classification System using Neural Network Trained with Particle Swarm Optimization
论文作者
论文摘要
先前发现心脏病发作的可能会导致挽救一个人的生命。将特定的标准置于提供即将出现的预警预警的系统中，这对于即将进行的心脏病发作的更好的预防计划将是有利的。为此，已经进行了一些研究，但是尚未达到目标以防止患者患这种疾病。在本文中，使用粒子群优化训练的神经网络（PSONN）用于分析输入标准并增强心脏病发作预期。使用了疾病记录的真实和新颖的数据集。预处理数据后，将馈入系统。结果，PSONN的结果已针对其他算法的结果进行了评估。在受雇的那些人中，决策树，随机森林，接受反向传播的神经网络（BPNN）和幼稚的贝叶斯。然后产生有关上述算法的100％，99.2424％，99.2323％，81.3131％和66.4141％的结果，这表明PSONN在所有其他测试算法中都记录了最高的准确率。
The prior detection of a heart attack could lead to the saving of one's life. Putting specific criteria into a system that provides an early warning of an imminent at-tack will be advantageous to a better prevention plan for an upcoming heart attack. Some studies have been conducted for this purpose, but yet the goal has not been reached to prevent a patient from getting such a disease. In this paper, Neural Network trained with Particle Swarm Optimization (PSONN) is used to analyze the input criteria and enhance heart attack anticipation. A real and novel dataset that has been recorded on the disease is used. After preprocessing the data, the features are fed into the system. As a result, the outcomes from PSONN have been evaluated against those from other algorithms. Decision Tree, Random Forest, Neural network trained with Backpropagation (BPNN), and Naive Bayes were among those employed. Then the results of 100%, 99.2424%, 99.2323%, 81.3131%, and 66.4141% are produced concerning the mentioned algorithms, which show that PSONN has recorded the highest accuracy rate among all other tested algorithms.
