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- Vol. 6 Issue 5
- Article
A Comprehensive Evaluation of Machine Learning Vs. Statistical Learning for Medical Insurance Premium Prediction
Authors
1 College of Management and Economics, University of Kirkuk, Kirkuk, Iraq. 2 College of Pharmacy, University of Kirkuk, Kirkuk, Iraq. 3 College of Computer Science and Information Technology, University of Kirkuk, Kirkuk, Iraq.
Article Information
- Published In
- Volume 6, Issue 5 (2026)
- Published Online
- September-October 2026
- Pages
- 527-533
- Article Type
- Original Article
References
1. K. Kaushik, A. Bhardwaj, A. D. Dwivedi, and R. Singh, “Article Machine Learning-Based Regression Framework to Predict Health Insurance Premiums,” International Journal of Environmental Research and Public Health, vol. 19, no. 13, 2022, doi: 10.3390/ijerph19137898.
2. “Health Insurance Premium Prediction with Machine Learning. Available online,” https://thecleverprogrammer.com/2021/10/26 /health-insurance-premium-prediction-with-machine-learning/ (accessed on 9 May 2022), 2022. .
3. T. techniques. CHARLEONNAN, A.; FUFAUNG, T.; NIYOMWONG, “Predictive analytics for chronic kidney disease using machine learning,” Management and Innovation Technology International Conference, MITiCON 2016, pp. MIT80–MIT83,., 2017.
4. S. PITCHUMANI ANGAYARKANNI, “Predictive analytics of chronic kidney disease using machine learning algorithm.,” International Journal of Recent Technology and Engineering, vol. 8, no. n.2, pp. 940–947, 2019.
5. W. V Sivarajah U, Kamal M, Irani Z, “Critical analysis of big data challenges and analytical methods.,” J Bus Res, vol. 70, pp. 263–286, 2017.
6. P. N. Joly Y, Burton H, Irani Z, Knoppers B, Feze I, Dent T, M. Chowdhury S, Foulkes W, Hall A, Hamet P, Kirwan N, and H. I. A, Simard J, “Life Insurance: genomicsStratification and risk classification.,” Eur J Hum Genet, vol. 22, pp. 575–579, 2014.
7. J. D. Umamaheswari K, “Role of data mining in Insurance Industry.,” Int J Adv Comput Technol, vol. 3, pp. 961–966, 2014.
8. J. P. Raj A, “Changing face of the Insurance Industry,” https://www.infosys.com/industries/insurance/ white-papers/Documents/changing-face-insurance-industry.pdf, 2017. .
9. H. Fauzan, M.A.; Murfi, “The Accuracy of XGBoost for Insurance Claim Prediction.,” Int. J. Adv. Soft Comput. Appl. . Available online: https://www.claimsjournal.com/news/national/2013/11/21/240353.htm (accessed on 9 May 2022)., vol. 10, pp. 159–171, 2018.
10. J. J. Sun, “Identification and Prediction of Factors Impact America Health Insurance Premium.,” 2020.
11. A. Kumar Sharma, D.; Sharma, “Prediction of Health Insurance Emergency Using Multiple Linear Regression,” Technique. Eur. J. Mol. Clin. Med., vol. 7, pp. 98–105, 2020.
12. Y. Yang, W. Qian, and H. Zou, “Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models,” Journal of Business and Economic Statistics, vol. 36, no. 3, pp. 456–470, 2018, doi: 10.1080/07350015.2016.1200981.
13. C. F. Kurz, “Tweedie distributions for fitting semicontinuous health care utilization cost data,” BMC Medical Research Methodology, vol. 17, no. 1, pp. 1–9, 2017, doi: 10.1186/s12874-017-0445-y.
14. R. Hoerl, A. and Kennard, “Ridge regression: Biased estimation for nonorthogonal problems.,” Technometrics, vol. 12, no. 1, pp. 55–67, 1970.
15. S. Methods, “Regularization Shrinkage Methods,” pp. 1–45, 2021.
16. F. Corea, “Introduction to Data,” pp. 1–5, 2019, doi: 10.1007/978-3-030-04468-8_1.
17. G. K. Smyth, “Regression Analysis of Quantity Data with Exact Zeros,” In Proceedings of the Second Australia-Japan Workshop on Stochastic Models in Engineering, Technology, and Management, edited by R. J. Wilson, S. Osaki, and D. N. P. Murthy, 572–580. Queensland, Australia: Technology Management Centre, University of Queen, 1996.
18. J. K. Tay, B. Narasimhan, and T. Hastie, “Elastic Net Regularization Paths for All Generalized Linear Models,” 2021, [Online]. Available: http://arxiv.org/abs/2103.03475.
19. W. A. Hadba, “Tweedie regularization model and application,” Communications in Statistics - Theory and Methods, vol. 0, no. 0, pp. 1–18, 2024, doi: 10.1080/03610926.2024.2349698.
20. A. S. Romana, “A Comparative Study of Different Machine Learning Algorithms for Disease Prediction,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 7, no. 7, p. 172, 2017, doi: 10.23956/ijarcsse/v7i7/0177.
21. B.-H. W. Mevik, “Introduction to the pls Package,” Cran, no. Section 7, pp. 1–24, 2022, [Online]. Available: https://cran.r-project.org/web/packages/pls/vignettes/pls-manual.pdf.
22. E. Edeh, W.-J. Lo, and J. Khojasteh, Review of Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook, vol. 30, no. 1. 2023.
23. D. M. Pirouz, “An Overview of Partial Least Squares,” SSRN Electronic Journal, no. March, 2012, doi: 10.2139/ssrn.1631359.
24. J. G. Sánchez León, “Machine Learning and Neural Networks,” Mathematica Beyond Mathematics, pp. 207–230, 2022, doi: 10.1201/9781003176800-6.
25. and A. B. Bruce, Peter, Practical Statistics for Data Scientists. O’Reilly Media. 2017.
26. R. James, G., Witten, D., Hastie, T., Tibshirani, An Introduction to Statistical Learning - with Applications in R | Gareth James | Springer. 2013.
27. R. M. Golden, “Statistical Machine Learning,” Statistical Machine Learning, 2020, doi: 10.1201/9781351051507.
28. B. Smola, A.J., Scholkopf, “A tutorial on support vector regression.,” Statistics and Computing, vol. 12, no. 3, pp. 199–222, 2004.
29. B. Sekeroglu, Y. K. Ever, K. Dimililer, and F. Al-Turjman, “Comparative Evaluation and Comprehensive Analysis of Machine Learning Models for Regression Problems,” Data Intelligence, vol. 4, no. 3, pp. 620–652, 2022, doi: 10.1162/dint_a_00155.
30. C.-J. Lin∗ and and R. C. Weng, “Simple Probabilistic Predictions for Support Vector Regression,” National Taiwan University, Taipei 106, Taiwan (cjlin@csie.ntu.edu.tw) †Department of Statistics National Chengchi University, Taipei 116, Taiwan, no. 101, 2004.
31. H. K. Karatzoglou A, Meyer D, “Support Vector Machines in R,” Journal of Statistical Software, vol. 15, pp. 1–28, 2006.
32. A. Kassambara, “Machine Learning Essentials. Practical Guide in R.,” Sthda. p. 210, 2017, [Online]. Available: https://www.m-culture.go.th/mculture_th/download/king9/Glossary_about_HM_King_Bhumibol_Adulyadej’s_Funeral.pdf.
2. “Health Insurance Premium Prediction with Machine Learning. Available online,” https://thecleverprogrammer.com/2021/10/26 /health-insurance-premium-prediction-with-machine-learning/ (accessed on 9 May 2022), 2022. .
3. T. techniques. CHARLEONNAN, A.; FUFAUNG, T.; NIYOMWONG, “Predictive analytics for chronic kidney disease using machine learning,” Management and Innovation Technology International Conference, MITiCON 2016, pp. MIT80–MIT83,., 2017.
4. S. PITCHUMANI ANGAYARKANNI, “Predictive analytics of chronic kidney disease using machine learning algorithm.,” International Journal of Recent Technology and Engineering, vol. 8, no. n.2, pp. 940–947, 2019.
5. W. V Sivarajah U, Kamal M, Irani Z, “Critical analysis of big data challenges and analytical methods.,” J Bus Res, vol. 70, pp. 263–286, 2017.
6. P. N. Joly Y, Burton H, Irani Z, Knoppers B, Feze I, Dent T, M. Chowdhury S, Foulkes W, Hall A, Hamet P, Kirwan N, and H. I. A, Simard J, “Life Insurance: genomicsStratification and risk classification.,” Eur J Hum Genet, vol. 22, pp. 575–579, 2014.
7. J. D. Umamaheswari K, “Role of data mining in Insurance Industry.,” Int J Adv Comput Technol, vol. 3, pp. 961–966, 2014.
8. J. P. Raj A, “Changing face of the Insurance Industry,” https://www.infosys.com/industries/insurance/ white-papers/Documents/changing-face-insurance-industry.pdf, 2017. .
9. H. Fauzan, M.A.; Murfi, “The Accuracy of XGBoost for Insurance Claim Prediction.,” Int. J. Adv. Soft Comput. Appl. . Available online: https://www.claimsjournal.com/news/national/2013/11/21/240353.htm (accessed on 9 May 2022)., vol. 10, pp. 159–171, 2018.
10. J. J. Sun, “Identification and Prediction of Factors Impact America Health Insurance Premium.,” 2020.
11. A. Kumar Sharma, D.; Sharma, “Prediction of Health Insurance Emergency Using Multiple Linear Regression,” Technique. Eur. J. Mol. Clin. Med., vol. 7, pp. 98–105, 2020.
12. Y. Yang, W. Qian, and H. Zou, “Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models,” Journal of Business and Economic Statistics, vol. 36, no. 3, pp. 456–470, 2018, doi: 10.1080/07350015.2016.1200981.
13. C. F. Kurz, “Tweedie distributions for fitting semicontinuous health care utilization cost data,” BMC Medical Research Methodology, vol. 17, no. 1, pp. 1–9, 2017, doi: 10.1186/s12874-017-0445-y.
14. R. Hoerl, A. and Kennard, “Ridge regression: Biased estimation for nonorthogonal problems.,” Technometrics, vol. 12, no. 1, pp. 55–67, 1970.
15. S. Methods, “Regularization Shrinkage Methods,” pp. 1–45, 2021.
16. F. Corea, “Introduction to Data,” pp. 1–5, 2019, doi: 10.1007/978-3-030-04468-8_1.
17. G. K. Smyth, “Regression Analysis of Quantity Data with Exact Zeros,” In Proceedings of the Second Australia-Japan Workshop on Stochastic Models in Engineering, Technology, and Management, edited by R. J. Wilson, S. Osaki, and D. N. P. Murthy, 572–580. Queensland, Australia: Technology Management Centre, University of Queen, 1996.
18. J. K. Tay, B. Narasimhan, and T. Hastie, “Elastic Net Regularization Paths for All Generalized Linear Models,” 2021, [Online]. Available: http://arxiv.org/abs/2103.03475.
19. W. A. Hadba, “Tweedie regularization model and application,” Communications in Statistics - Theory and Methods, vol. 0, no. 0, pp. 1–18, 2024, doi: 10.1080/03610926.2024.2349698.
20. A. S. Romana, “A Comparative Study of Different Machine Learning Algorithms for Disease Prediction,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 7, no. 7, p. 172, 2017, doi: 10.23956/ijarcsse/v7i7/0177.
21. B.-H. W. Mevik, “Introduction to the pls Package,” Cran, no. Section 7, pp. 1–24, 2022, [Online]. Available: https://cran.r-project.org/web/packages/pls/vignettes/pls-manual.pdf.
22. E. Edeh, W.-J. Lo, and J. Khojasteh, Review of Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook, vol. 30, no. 1. 2023.
23. D. M. Pirouz, “An Overview of Partial Least Squares,” SSRN Electronic Journal, no. March, 2012, doi: 10.2139/ssrn.1631359.
24. J. G. Sánchez León, “Machine Learning and Neural Networks,” Mathematica Beyond Mathematics, pp. 207–230, 2022, doi: 10.1201/9781003176800-6.
25. and A. B. Bruce, Peter, Practical Statistics for Data Scientists. O’Reilly Media. 2017.
26. R. James, G., Witten, D., Hastie, T., Tibshirani, An Introduction to Statistical Learning - with Applications in R | Gareth James | Springer. 2013.
27. R. M. Golden, “Statistical Machine Learning,” Statistical Machine Learning, 2020, doi: 10.1201/9781351051507.
28. B. Smola, A.J., Scholkopf, “A tutorial on support vector regression.,” Statistics and Computing, vol. 12, no. 3, pp. 199–222, 2004.
29. B. Sekeroglu, Y. K. Ever, K. Dimililer, and F. Al-Turjman, “Comparative Evaluation and Comprehensive Analysis of Machine Learning Models for Regression Problems,” Data Intelligence, vol. 4, no. 3, pp. 620–652, 2022, doi: 10.1162/dint_a_00155.
30. C.-J. Lin∗ and and R. C. Weng, “Simple Probabilistic Predictions for Support Vector Regression,” National Taiwan University, Taipei 106, Taiwan (cjlin@csie.ntu.edu.tw) †Department of Statistics National Chengchi University, Taipei 116, Taiwan, no. 101, 2004.
31. H. K. Karatzoglou A, Meyer D, “Support Vector Machines in R,” Journal of Statistical Software, vol. 15, pp. 1–28, 2006.
32. A. Kassambara, “Machine Learning Essentials. Practical Guide in R.,” Sthda. p. 210, 2017, [Online]. Available: https://www.m-culture.go.th/mculture_th/download/king9/Glossary_about_HM_King_Bhumibol_Adulyadej’s_Funeral.pdf.
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