Insuring Smiles: Predicting routine dental coverage using Spark ML
Aishwarya Gupta Rahul S. Bhogale Priyanka Thota Prathushkumar Dathuri Jongwook Woo
Finding suitable health insurance coverage can be challenging for individuals
and small enterprises in the USA. The Health Insurance Exchange Public Use
Files (Exchange PUFs) dataset provided by CMS offers valuable information on
health and dental policies [1]. In this paper, we leverage machine learning
algorithms to predict if a health insurance plan covers routine dental services
for adults. By analyzing plan type, region, deductibles, out-of-pocket
maximums, and copayments, we employ Logistic Regression, Decision Tree, Random
Forest, Gradient Boost, Factorization Model and Support Vector Machine
algorithms. Our goal is to provide a clinical strategy for individuals and
families to select the most suitable insurance plan based on income and
expenses.