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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21508
Article Published : 8513
Total Authors : 22399
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| Paper Title: | Medical Insurance Premium Prediction using Regression Models |
| Authors Name: | Kamma Lakshmi Narayana , Yogesh , Putta Kowshik |
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IJRTI_186190
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| Published Paper Id: | IJRTI2304248 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | Medical insurance premium prediction is an important area of research that aims to accurately estimate the cost of healthcare for individuals based on their medical history and other relevant factors. In this study, It is considered the performance of four different machine learning models for medical insurance premium prediction: linear regression, ridge regression, support vector machine (SVM), and random forest regression. The usage of a dataset of medical insurance claims and demographic information to train and evaluate the models. The data was preprocessed and feature engineered to ensure optimal model performance. We used mean squared error (MSE) and R-squared as evaluation metrics to compare the performance of the different models. Our results showed that all four models were able to predict medical insurance premiums with reasonable accuracy. However, SVM and random forest regression outperformed linear and ridge regression in terms of MSE and R-squared. SVM had the lowest MSE of 3122.55 and R-squared of 0.854, while random forest regression had an MSE of 3423.32 and the highest R-squared of 0.887. Overall, The study highlights the potential of machine learning models for medical insurance premium prediction. SVM and random forest regression are promising techniques that can accurately estimate medical insurance premiums based on a range of factors. These models can provide valuable insights for healthcare providers and policy makers to improve the cost-effectiveness and quality of healthcare services |
| Keywords: | SVM, Random Forest Regression, MSE |
| Cite Article: | "Medical Insurance Premium Prediction using Regression Models", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 4, page no.1512 - 1517, April-2023, Available :http://www.ijrti.org/papers/IJRTI2304248.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2304248
Registration ID:186190
Published In: Volume 8 Issue 4, April-2023
DOI (Digital Object Identifier):
Page No: 1512 - 1517 Country: Prakasam, Andhra Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2304248 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2304248 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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