Optimizing Health Insurance Costs through ML Based Predictive Models: A Study of Key Socio-Economic Factors

• Author(s): Paulami Bandyopadhyay ; Paramita Banerjee
• Paper ID: 1701486
• Page: 725-728
• Published Date: 04-09-2019
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 2 August-2019

Abstract

The goal of this study is to forecast health insurance costs by analyzing how different socioeconomic and individual factors affect premiums. The study takes into account factors like age, gender, body fat percentage, family size, smoking habits, and geographic location using data from a representative health insurance company. To model and forecast insurance costs, a machine learning technique—more especially, the linear regression model—was used. As a result, the study concluded that smoking has the biggest impact on premiums, followed by age and body fat percentage. Furthermore, research indicates that women and people from the Southeast region of the United States are more likely to choose plans with higher premiums. This study helps firms improve risk assessment and rate setting by providing insightful information for theoretical analysis and real-world applications in the insurance sector. The study also emphasizes how advancements in AI and machine learning are changing the way that health insurance is administered. Insurers can provide policyholders with individualized, effective, and expedited service thanks to regression-based models. These models improve insurers' capacity to develop precise, tailored policies and expedite service by examining variables like age, gender, body mass index, number of children, smoking habits, and geolocation. All things considered, AI-driven insights are simplifying the relationship between policyholders and insurers and assisting in the better prediction and management of health risks and expenses.

Keywords

Machine Learning, AI, Healthcare, Predictive Models, AI in Healthcare

Citations

IRE Journals:
Paulami Bandyopadhyay , Paramita Banerjee "Optimizing Health Insurance Costs through ML Based Predictive Models: A Study of Key Socio-Economic Factors" Iconic Research And Engineering Journals Volume 3 Issue 2 2019 Page 725-728

IEEE:
Paulami Bandyopadhyay , Paramita Banerjee "Optimizing Health Insurance Costs through ML Based Predictive Models: A Study of Key Socio-Economic Factors" Iconic Research And Engineering Journals, 3(2)