Insuring Smiles: Predicting routine dental coverage using Spark ML
This provides a tool for individuals and small enterprises to select insurance plans, but it is incremental as it applies existing methods to a new dataset.
The paper tackles the problem of predicting whether US health insurance plans cover routine dental services for adults by analyzing plan characteristics like deductibles and copayments using six standard machine learning algorithms, achieving unspecified predictive performance.
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.