Background and purpose: Heart disease has been one of the most important causes of death in the last 10 years, so the use of classification methods to diagnose and predict heart disease is very important. If this disease is predicted before menstruation, it is possible to prevent high mortality of the disease and provide more accurate and efficient treatment methods. Materials and Methods: Due to the selection of input features, the use of basic algorithms can be very time-consuming. Reducing dimensions or choosing a good subset of features, without risking accuracy, has great importance for basic algorithms for successful use in the region. In this paper, we propose an ensemble-genetic learning method using wrapper feature reduction to select features in disease classification. Findings: The development of a medical diagnosis system based on ensemble learning to predict heart disease provides a more accurate diagnosis than the traditional method and reduces the cost of treatment. Conclusion: The results showed that Thallium Scan and vascular occlusion were the most important features in the diagnosis of heart disease and can distinguish between sick and healthy people with 97.57% accuracy.
翻译:背景和目的:心脏病是过去10年来最重要的死亡原因之一,因此使用分类方法诊断和预测心脏病非常重要。如果在月经前预测出这一疾病,则有可能预防该疾病的高死亡率,并提供更准确有效的治疗方法。材料和方法:由于选择了输入特征,使用基本算法可能非常费时。降低尺寸或选择一大批特征对于本区域成功使用的基本算法非常重要,因此,使用分类方法诊断和预测心脏病非常重要。在本文中,我们建议采用一种混合基因学习方法,使用包装特征减少法来选择疾病分类的特征。结果:基于共同学习来预测心脏病的医疗诊断系统的发展提供了比传统方法更准确的诊断,并降低了治疗费用。结论:结果显示,Tallium扫描和血管隔离是诊断心脏病的最重要特征,可以将病人和健康者区分为97.57%的准确度。