Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease
Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease
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DOI：
10.1007/s10489-017-1037-6
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发表时间：
2018-07-01
影响因子：
5.3
通讯作者：
Murase, Kazuyuki
中科院分区：
文献类型：
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作者：
Paul, Animesh Kumar;Shill, Pintu Chandra;Murase, Kazuyuki
由于信息模糊、决策不精确和不确定，专家的知识库系统不能有效地辅助医生对心脏病进行准确的诊断和治疗。因此，自动诊断模糊系统非常需要时间来提高诊断精度。在本文中，我们开发了一种基于遗传算法（GA）和改进的动态多群粒子群优化（MDMS-PSO）的自动模糊诊断系统，用于预测心脏病的风险水平。我们提出的模糊诊断系统（FS）的工作原理如下：i）预处理数据集ii）通过统计方法选择有效属性，例如相关系数，R平方和加权最小二乘（WLS）方法，iii）使用GA在所选属性的基础上形成加权模糊规则，iv）采用MDMS-PSO来优化隶属函数（MF） FS，v) 通过融合不同的局部 FS，从生成的模糊知识库构建集成 FS。最后，为了确定自适应FS的效率，通过对公开的不同现实数据集进行定量、定性和比较分析来评估FS的适用性。从实证分析中，我们看到这种混合模型可以精确地管理知识模糊性和决策不确定性，并且在不同的公开可用的心脏病数据集上比其他现有方法产生了更好的准确性，从而证明了其对不同数据集的适应性。
Expert's knowledge base systems are not effective as a decision-making aid for physicians in providing accurate diagnosis and treatment of heart diseases due to vagueness in information and impreciseness and uncertainty in decision making. For this reason, automatic diagnostic fuzzy systems are very time demanding to improve the diagnostic accuracy. In this paper, we have developed an automatic fuzzy diagnostic system based on genetic algorithm (GA) and a modified dynamic multi-swarm particle swarm optimization (MDMS-PSO) for prognosticating the risk level of heart disease. Our proposed fuzzy diagnostic system (FS) works as follows: i) Preprocess the data sets ii) Effective attributes are selected through statistical methods such as Correlation coefficient, R-Squared and Weighted Least Squared (WLS) method, iii) Weighted fuzzy rules are formed on the basis of selected attributes using GA, iv) MDMS-PSO is employed for the optimization of membership functions (MFs) of FS, v) Build the ensemble FS from the generated fuzzy knowledge base by fusing the different local FSs. Finally, to ascertain the efficiency of the adaptive FS, the applicability of the FS is appraised with quantitative, qualitative and comparative analysis on the publicly available different real-life data sets. From the empirical analysis, we see that this hybrid model can manage the knowledge vagueness and decision-making uncertainty precisely and it has yielded better accuracy on the different publicly available heart disease data sets than other existing methods so that it justifies its adaptability with different data sets.