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Machine Learning to Forecast the cost of Health Insurance

Authors: Keerthana Prathap; Bijimol TK;

Machine Learning to Forecast the cost of Health Insurance

Abstract

Abstract— It is a significant difficulty for the insurance industry to charge each customer a premium that is reasonable for the risk that they represent. The insurer's management results and financial statements are significantly obstructed by how accurately the claim amount is projected. Estimating the cost of claims in an insurance company is a real problem that needs a more precise and automated solution. A lot of factors influence the cost of claims based on health characteristics such as age, sex, weight, BMI, hereditary disorders, the number of dependents, blood pressure, smokers, region, regular exercise, and others. The cost of health insurance is analyzed and projected by insurance companies using a variety of techniques. This study demonstrates the ability of various models to predict insurance premiums.

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Keywords

Insurance, Random Forest, SVM, Gradient Boosting, Regression

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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