Modified adaptive neuro-fuzzy inference system (M-ANFIS) based multi-disease analysis of healthcare Big Data

Abstract

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.