Abstract
Healthcare is essential in pandemic times, but it is crucial for the well-being
of daily life. Many countries allocate substantial funds towards providing high-quality
healthcare services. As healthcare expenses escalate, policymakers and funders are
increasingly focused on investigating the underlying factors driving the high costs of
medical resources. A comprehensive analysis carried the required expenses towards
identification, valuation, and measurement of resources utilized for the diagnosis
process. The objective of the chapter is to provide how the data analysis is carried
which helps to identify fraudulent behaviors. The generated model assists health
management organizations in identifying suspicious behaviors toward claims.
Healthcare fraud is a severe threat to global health results, and could lead to misuse,
scarce resources, and negative impacts on healthcare access, infrastructures, and social
determinants of health. Healthcare fraud is associated with increased healthcare costs in
most of the leading countries. The proposed research work provides an estimation
mechanism for utilizing health resources and their impacts on healthcare costs. This
chapter proposes strategic ways of handling healthcare data to prevent future healthcare
fraud, decrease healthcare expenditure, and adequately use resources to benefit the
population. This chapter works on three primary datasets and a synthetic dataset
aggregated from the primary datasets. The data preprocessing is carried out at different
levels of the model, which truly enhances the data quality. The model is constructed at
three levels; the first level analyzes datasets in which it extracts the primary features
and provides constructive decisions and outcomes on the processing of data.
Regressive analysis of the hierarchical grouping mechanism helps to know the detailed
features that could affect healthcare and prevent resource misuse.