MEDICAL INSURANCE COST ANALYSIS AND PREDICTION USINGEXTREME GRADIENT BOOSTING ALGORITHM
DOI:
https://doi.org/10.29121/shodhkosh.v5.i6.2024.2595Keywords:
Medical Insurance, Machine Learning, Model Build, Gradient Boosting, Data Science, EngineeringAbstract [English]
An insurance policy lowers or completely removes the costs related to declining returns caused by different risks. A variety of things affect the cost of insurance. These elements have an impact on how insurance plans are made. In the insurance industry, machine learning (ML) has promise for increasing the effectiveness of insurance policy terms. Actual modelling of insurance claims has emerged as a major field of study in the health insurance industry in recent years, primarily for the purpose of determining appropriate rates. This is essential for drawing in new insured’s, keeping the ones you already have, and managing current plan participants well. However, it can be difficult to create an accurate forecast model for medical insurance prices because of the multitude of factors that influence them and their inherent complexities. The expected costs of health insurance could be greatly impacted by a number of factors, such as provider characteristics, lifestyle decisions, health status, accessibility in a given area, and demographic information. Actuarial research into predictive modeling in healthcare is still going strong, as more insurance companies look to leverage ML technologies to increase productivity and efficiency. Regression-based ensemble machine learning models that incorporate different Extreme Gradient Boosting (XGBoost) techniques are used in this study to forecast medical insurance expenses.
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Copyright (c) 2024 Dr S G Balakrishnan, Abdulla N, Hari Krishnan S, Gokul V, Amizhthan S P
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