Medical Costs Estimation Using Linear Regression Method

Written by Ni Made Dita Dwikasari, Ni Putu Sutramiani, Komang Sri Yanisa Putri, Nyoman Tri Rahaditya Kusuma, Made Dimas Aldi Dwi Pramana, I Wayan Agus Surya Darma
on December 25, 2023

Authors:

Ni Made Dita Dwikasari, Ni Putu Sutramiani, Komang Sri Yanisa Putri, Nyoman Tri Rahaditya Kusuma, Made Dimas Aldi Dwi Pramana, I Wayan Agus Surya Darma

Abstract:

“Medical costs are a significant issue in the health sector. High healthcare cost lead to the need to anticipate financial risks for individuals and insurance providers. Therefore, medical cost data analysis is necessary to estimate future medical expenses. This research implements data mining techniques using Simple and Multiple Linear Regression methods to estimate medical costs. The dataset used consists of insurance claim data obtained from Kaggle, which includes attributes such as age, gender, body mass index, number of children, smoking habits, region, and medical charges. The research findings that Multiple Linear Regression outperforms Simple Linear Regression in estimating the provided dataset, with R2 value of 80% and lower ?? MSE and MAE values than Simple Linear Regression. The application of linear regression in insurance claim data analysis can provide significant benefits for patients, hospitals, and insurance providers. Overall, this research highlights the effectiveness of data mining techniques, specifically linear regression, in estimating healthcare costs.”

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PDF:

https://jurnal.harianregional.com/merpati/full-108754

Published

2023-12-25

How To Cite

DWIKASARI, Ni Made Dita et al. Medical Costs Estimation Using Linear Regression Method.Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi), [S.l.], v. 11, n. 3, p. 171-179, dec. 2023. ISSN 2685-2411. Available at: https://jurnal.harianregional.com/merpati/id-108754. Date accessed: 08 Jul. 2024. doi:https://doi.org/10.24843/JIM.2023.v11.i03.p03.

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Issue

Vol 11 No 3 (2023): Vol. 11, No. 3, December 2023

Section

Articles

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License