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Forecasting Medical Insurance Claim Cost with Data Mining Techniques

Sahare, Aditya Naresh (2023) Forecasting Medical Insurance Claim Cost with Data Mining Techniques. Masters thesis, Dublin, National College of Ireland.

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In the healthcare sector, data mining has lately become essential for getting vital information. When developing medical facilities, the cost of health insurance is quite significant. To provide better medical treatment, it is essential to forecast health insurance costs, one of the ways to upgrade medical facilities. Estimating the patient-paid component of health insurance premiums is the essay’s focus. The inability to charge each customer a premium enough for the risk they represent is a serious problem for the insurance sector. A Kaggle dataset of 15000 records of customer’s medical history in USA is used in research. The selected machine learning techniques are Linear Regression (with and without Log Transformed Dependent Variable), Linear Regression with Interaction of Independent variables, Lasso Regression, Elastic Net Regression and Ridge Regression. The performance was found to best for Linear Regression Model with Interaction of Independent variables with R2 score of 0.79 achieved by the model.

Item Type: Thesis (Masters)
Muntean, Cristina Hava
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
R Medicine > Healthcare Industry
H Social Sciences > HG Finance > Insurance
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 25 May 2023 14:33
Last Modified: 25 May 2023 14:33

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