论文标题
使用贝叶斯网络模型的心脏病预测的快速算法
A Fast Algorithm for Heart Disease Prediction using Bayesian Network Model
论文作者
论文摘要
心血管疾病是世界各地死亡的第一大原因。数据挖掘可以帮助从卫生部门的可用数据中检索有价值的知识。它有助于训练模型以预测患者的健康状况，与临床实验相比，这将更快。机器学习算法的各种实现，例如逻辑回归，K-Nearest邻居，幼稚贝叶斯（NB），支持向量机等，已应用于Cleveland Heart数据集中，但使用Bayesian Network（BN）建模已有限制。这项研究应用了BN建模，以发现从UCI存储库中收集的克利夫兰心脏数据的14个相关属性之间的关系。目的是检查属性之间的依赖性如何影响分类器的性能。 BN在属性之间产生可靠且透明的图形表示，并具有预测新方案的能力。该模型的精度为85％。结论是，该模型的表现优于NB分类器，该分类器的精度为80％。
Cardiovascular disease is the number one cause of death all over the world. Data mining can help to retrieve valuable knowledge from available data from the health sector. It helps to train a model to predict patients' health which will be faster as compared to clinical experimentation. Various implementation of machine learning algorithms such as Logistic Regression, K-Nearest Neighbor, Naive Bayes (NB), Support Vector Machine, etc. have been applied on Cleveland heart datasets but there has been a limit to modeling using Bayesian Network (BN). This research applied BN modeling to discover the relationship between 14 relevant attributes of the Cleveland heart data collected from The UCI repository. The aim is to check how the dependency between attributes affects the performance of the classifier. The BN produces a reliable and transparent graphical representation between the attributes with the ability to predict new scenarios. The model has an accuracy of 85%. It was concluded that the model outperformed the NB classifier which has an accuracy of 80%.
