Heart diseases are one of the crucial diseases that may cause fatality in both men and women. About 12 million deaths occur across the world due to heart diseases. With the advancement in information technology, it is possible for the Healthcare industry to store enormous volume of data containingmillions of patient’s medical information along with their treatment details. If utilized in an efficient manner, this information helps the doctors to diagnose the diseases in a precise manner. Data mining algorithms are employed to analyse huge data sets and to discover unseen patterns. Data mining plays an essential role in medical diagnosis. Doctors bank on different computer models which uses data mining algorithms to prefigure different kinds of diseases in patients. So, the need is to design a methodical data mining algorithm that helps for better forecast of diseases. The main goal of this work is to create an ensemble of algorithms which results in better accuracy. The ensemble is constructed by making use of stacking ensemble technique, which comprises of two categorization algorithms namely Naïve Bayes and Artificial Neural Network. The Cleveland heart disease data set acquired from UCI machine learning repository containing 14 attributes and 303 instances is given as input to these algorithms. From our experimental analysis it is evident that the proposed ensemble scheme results in a better accuracy.

中文翻译：

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使用堆叠集成技术预测心脏病

心脏病是可能导致男性和女性死亡的重要疾病之一。由于心脏病，全世界大约有1200万人死亡。随着信息技术的发展，医疗保健行业有可能存储海量数据，其中包含数百万患者的医疗信息以及他们的治疗细节。如果以有效的方式使用，此信息将帮助医生以精确的方式诊断疾病。数据挖掘算法用于分析巨大的数据集并发现看不见的模式。数据挖掘在医学诊断中起着至关重要的作用。医生使用不同的计算机模型，这些模型使用数据挖掘算法来预知患者的各种疾病。所以，需要设计一种系统的数据挖掘算法，以帮助更好地预测疾病。这项工作的主要目的是创建一组算法，以提高准确性。该集合是利用堆叠集合技术构建的，该技术包括两种分类算法，即朴素贝叶斯算法和人工神经网络。从包含14个属性和303个实例的UCI机器学习存储库中获取的克利夫兰心脏病数据集被作为这些算法的输入。从我们的实验分析中可以明显看出，所提出的集成方案具有更好的准确性。其中包含两种分类算法，即朴素贝叶斯算法和人工神经网络。从包含14个属性和303个实例的UCI机器学习存储库中获取的克利夫兰心脏病数据集被作为这些算法的输入。从我们的实验分析中可以明显看出，提出的集成方案具有更好的准确性。其中包含两种分类算法，即朴素贝叶斯算法和人工神经网络。从包含14个属性和303个实例的UCI机器学习存储库中获取的克利夫兰心脏病数据集被作为这些算法的输入。从我们的实验分析中可以明显看出，所提出的集成方案具有更好的准确性。