Fetching useful information from big medical datasets is a complicated task in the big data age. Various classification algorithms are used in the data mining process to analyze information from the big medical dataset. Nevertheless, these classification algorithms are insufficient to handle big medical data. This work proposes an efficient, ensemble-based classification framework for big medical data to deal with this problem. The proposed work involves initially applying the preprocessing technique to remove noise, missing values, and unwanted features from big medical data. The process selects a subset of classifiers from a pool of classifiers. The selected classifiers are combined to form a hybrid system for efficient classification. The methodology further involves incremental learning from data samples, explaining the predicted outputs, and achieving high classification performance. Java is used for simulation, and the Cleveland Heart Disease big dataset and Diabetes big dataset are used for classification. The experimental result shows that the proposed ensemble algorithm provides an efficient classification compared with existing algorithms based on accuracy, precision, F-measure, recall, and execution time.

中文翻译：

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一种高效的、基于集合的大医疗数据分类框架

在大数据时代，从大型医疗数据集中获取有用信息是一项复杂的任务。在数据挖掘过程中使用了各种分类算法来分析来自大型医疗数据集的信息。然而，这些分类算法不足以处理大的医疗数据。这项工作为大医疗数据提出了一个有效的、基于集合的分类框架来处理这个问题。拟议的工作涉及最初应用预处理技术从大医疗数据中去除噪声、缺失值和不需要的特征。该过程从分类器池中选择分类器子集。将选定的分类器组合起来形成一个混合系统，用于高效分类。该方法进一步涉及从数据样本中进行增量学习，解释预测输出，并实现高分类性能。Java用于模拟，Cleveland Heart Disease大数据集和Diabetes大数据集用于分类。实验结果表明，与基于准确率、精度、F-measure、召回率和执行时间的现有算法相比，所提出的集成算法提供了一种有效的分类方法。