Machine Learning Models implemented into GUI Application for Accurate Prediction of Health Insurance Charges

Kenche, Bhagyashree Madhumesh (2024) Machine Learning Models implemented into GUI Application for Accurate Prediction of Health Insurance Charges. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (1MB) | Preview

Abstract

Health Insurance plays the most important roles in lives for sustainability. This industry being the global need faces significant challenges as it lags in providing the insurance charges as per the customers need. So, in-order to bridge this gap between raw data and actionable insights, allowing the insurance providers to optimize their operations and pricing at the same time encouraging and empowering policyholders to take better informed decisions about their insurance and healthcare coverage. This project aims at creating a graphical user interface (GUI) Application for predicting the Health Insurance Charges in real time with significant percentage of accuracy. Analyzation of various features such as age, sex, bmi (body mass index), smoker status, number of children, region helps to study and evaluate the performance of different models, including linear regression, random forest, and ensemble models. The outcomes display the effectiveness of these models, precisely in detecting the most influential factors in predicting costs. This work is evaluated using metrics like Mean Absolute Error, Mean Squared Error and R-squared value. Additionally, the research highlights the necessity of data visualization in understanding patterns and distributions within dataset. Moreover, the study thrives to contribute as a basis towards improving real time estimation tools for health insurance costs, leading to spotlight the decision making process and improve health insurance transparency for individuals in Ireland and beyond. Furthermore, this project explores the reliability and potential improvements in the predictive modelling process to better serve the insurance industry.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Haque, Rejwanul UNSPECIFIED
Uncontrolled Keywords:	Health Insurance; Charges prediction; Machine Learning; Feature Engineering; Model Evaluation; Insurance Industry
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 Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	31 Jul 2025 13:45
Last Modified:	31 Jul 2025 13:45
URI:	https://norma.ncirl.ie/id/eprint/8388

Actions (login required)

View Item