Experimental analysis of Medicare data using hierarchical grouping mechanism

• Published in: International journal of intelligent unmanned systems 2020-01, Vol.8 (1), p.68-82
• Main Authors: Naga, Jyothi P, Rajya, Lakshmi D, Rama Rao KVSN
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Big Data; Classification; Columns (structural); Computation; Data analysis; Datasets; Decision making; Decision support systems; Decomposition; Deep learning; Health care expenditures; Health care policy; Health insurance; Insurance claims; Insurance industry; Investigations; Machine learning; Medicare; Multidimensional data; Outliers (statistics); Time series
• ISSN: 2049-6427; 2049-6427
• DOI: 10.1108/IJIUS-03-2019-0019

Purpose Analyzing medicare data is a role undertaken by the government and commercial companies for accepting the appeals and sanctioning the claims of those insured under Medicare. As the data of medicare is robust and made up of heterogeneous typed columns, traditional approaches consist of a laborious and time-consuming process. The understanding and processing of such data sets and finding the role of each attribute for data analysis are tricky tasks which this research will attempt to ease. The paper aims to discuss these issues. Design/methodology/approach This paper proposes a Hierarchical Grouping (HG) with an experimental model to handle the complex data and analysis of the categorical data which consist of heterogeneous typed columns. The HG methodology starts with feature subset selection. HG forms a structure by quantitatively estimating the similarities and forms groups of the features for data. This is carried by applying metrics like decomposition; it splits the dataset and helps to analyze thoroughly under different labels with different selected attributes of Medicare data. The method of fixed regression includes metrics of re-indexing and grouping which works well for multiple keys (multi-index) of categorical data. The final stage of structure is applying multiple aggregation function on each attribute for quantitative computation. Findings The data are analyzed quantitatively with the HG mechanism. The results shown in this paper took less computation cost and speed, which are usually incurred on the publicly available data sets. Practical implications The motive of this paper is to provide a supportive work for the tasks like outlier detection, prediction, decision making and prescriptive tasks for multi-dimensional data. Originality/value It provides a new efficient approach to analyze medicare data sets.