Medical Insurance Cost Prediction
Sabarinath U S1, Ashly Mathew2
1Sabarinath U S, Department of Computer Science, St. Albert’s College, Kochi (Kerala), India.
2Ashly Mathew, Department of Computer Science, St. Albert’s College, Kochi (Kerala), India.
Manuscript received on 04 April 2024 | Revised Manuscript received on 29 May 2024 | Manuscript Accepted on 15 June 2024 | Manuscript published on 30 June 2024 | PP: 1-4 | Volume-4 Issue-4 June 2024 | Retrieval Number: 100.1/ijdcn.D503704040624 | DOI: 10.54105/ijdcn.D5037.04040624
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This is a medical insurance cost prediction model that uses a linear regression algorithm to predict the medical insurance charges of a person based on the given data. To predict things that have never been so easy. In this project used to predict values that wonder how Insurance amount is normally charged. This is a medical insurance cost prediction model that uses a linear regression algorithm to predict the medical insurance charges of a person based on the given data. This project on predicting medical insurance costs can serve various purposes and address several needs that are Accurate Pricing Insurance companies need accurate predictions of medical insurance costs to set appropriate premiums for policyholders. Predictive models can analyse historical data and various factors such as age, gender, pre-existing conditions, lifestyle habits, and geographic location to estimate future healthcare expenses accurately. This Prediction model achieves three regression methods accuracy that the linear regression gets an accuracy of 74.45 %, whereas Ridge regression and Support Vector Regression gets 82.59% word-level state-of-the-art accuracy. The Medical Insurance Cost Prediction project, proposes a comprehensive approach to predict the medical cost, aiming to develop a robust and accurate system capable of predicting the accurate cost for a particular individual. Leveraging linear regression, our proposed system builds upon the successes of existing models like different types of regressions like linear regression, Ridge regression and Support Vector regression. We will put the Regression algorithm into practice and evaluate how it performs in comparison to the other three algorithms. By comparing the performance of these three methodologies, this project aims to identify the most effective approach for medical insurance cost prediction. Through rigorous evaluation and validation processes, the selected model will provide valuable insights for insurance companies, policymakers, and individuals seeking to optimize healthcare resource allocation and financial planning strategies.
Keywords: Regression, Linear regression, Ridge Regression, Support vector Regression.
Scope of the Article: Machine/ Deep Learning with IoT & IoE