A health insurance premium is a monthly fee that is paid in a health plan to typically pay for medical, surgical, prescription drug and sometimes dental expenses incurred by the insured. Since 2010, the affordable care act has prohibited insurance companies from denying coverage to patients with pre-existing conditions and has allowed children to remain on their parents' insurance plans until they reached the age of 26. Creating the policy is a really important and challenging task. In order to determine health insurance premium quotes, there are several factors that have to be taken into consideration when defining a premium, such as pre-existing diseases, age, gender, family medical history, lifestyle, etc. In this paper, a combination of the K-means algorithm and the Elbow method is developed to accurately group people in an optimal number of clusters based on similarity. Based on this evaluation, the health insurance premium quote via the provided factors to predict the range of the health insurance premium quote for each group of people.

Clustering Application for Data-Driven Prediction of Health Insurance Premiums for People of Different Ages

As the scale of social health insurance fund continues to expand, inflation pressures and the challenges of an aging population, increasing the value of the Fund has become a priority work of social health insurance in the current and in the future. This paper analyzes the current feasibility and necessity of increasing the value of social health insurance fund and discussion how to increasing it on the basis of home and abroad experience about this.

The Discussion about preservation and increasing the value of Social health insurance fund

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The Discussion about preservation and increasing the value of Social health insurance fund