Big Data (BD) has turned into a significant research field owing to the dawn of vast quantity of data generated as of various sources like Internet of things (IoT), social media, and also multimedia applications. BD has played an imperative part in numerous decision-makings as well as forecasting domains for instance health care, recommendation systems, web display advertisement, transportation, clinicians, business analysis, and fraud detection along with tourism marketing. The domain of health care attained its influence by the effect of BD since the data sources concerned in the healthcare organizations are famous for their volume, heterogeneous complexity, and high dynamism. Though the function of BD analytical techniques, platforms, and tools are realized among various domains, their effect on healthcare organization for possible healthcare applications shows propitious research directions. This paper concentrates on the analysis of multiple diseases using modified adaptive neuro-fuzzy inference system (M-ANFIS). Initially, the healthcare BD undergoes pre-processing. In the pre-processing step, data format identification and integration of the healthcare BD dataset is done. Now, features are extracted from the preprocessed dataset and the count of the closed frequent item set (CFI) is found. Then, the entropy of the CFI count is determined. Finally, analyses of the multiple diseases are executed with the aid of M-ANFIS. In M-ANFIS, k-medoid clustering is used to cluster the CFI entropy of healthcare BD. The proposed method’s performance is assessed by comparing it with the other existent techniques.

中文翻译：

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基于改进的自适应神经模糊推理系统 (M-ANFIS) 的医疗大数据多疾病分析

由于物联网 (IoT)、社交媒体和多媒体应用等各种来源产生的大量数据的出现，大数据 (BD) 已成为一个重要的研究领域。BD 在许多决策和预测领域发挥了重要作用，例如医疗保健、推荐系统、网络显示广告、运输、临床医生、业务分析和欺诈检测以及旅游营销。医疗保健领域受到 BD 的影响，因为医疗保健组织中相关的数据源以其数量庞大、异构复杂性和高动态性而闻名。虽然BD分析技术、平台和工具的功能是跨域实现的，它们对可能的医疗保健应用的医疗保健组织的影响显示出有利的研究方向。本文专注于使用改进的自适应神经模糊推理系统 (M-ANFIS) 对多种疾病进行分析。最初，医疗保健 BD 进行预处理。在预处理步骤中，完成了医疗保健BD数据集的数据格式识别和整合。现在，从预处理的数据集中提取特征，并找到闭合频繁项集（CFI）的计数。然后，确定 CFI 计数的熵。最后，在 M-ANFIS 的帮助下对多种疾病进行分析。在 M-ANFIS 中，k-medoid 聚类用于聚类医疗保健 BD 的 CFI 熵。所提出的方法的性能是通过将其与其他现有技术进行比较来评估的。