Health insurance risk classification using multinomial logistic regression
Keywords:
Health insurance, classification, statistical learning, multinomial logistic regressionAbstract
In a health insurance portfolio, not all policyholders possess similar risk levels; specific individuals exhibit higher risk than others. Consequently, it might appear inequitable to impose the same premium on everyone. This diversity can be mitigated by employing risk categories that exhibit greater uniformity, considering factors such as gender, age, and other indicators. By applying risk classification, the expected cost for each risk category can be estimated using predefined methods. This study introduces an approach for categorizing insured individuals in health insurance based on statistical learning, specifically employing the multinomial logistic regression algorithm. The research underscores the significance of risk classification in establishing an equitable pricing structure.
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Charpentier, A., Denuit, M., & Elie, R. (2015). Segmentation et mutualisation les deux faces d’une même pièce. Risques n° 103, 19-23.
SAOUDI,A.,EL KASSIMI, F., ZAHI, J. (2023).Technical reserving in non-life insurance : a literature review of aggregated and individual methods. journal of integrated studies in economics, law, technical sciences & communication, Vol (1), No (2) 2023,1-12.
EL KASSIMI. F & ZAHI. J (2022). Proposition d’un modèle de tarification en assurance maladie obligatoire à travers le modèle linéaire généralisé . Alternatives managériales et économiques Vol 4, No 4(Octobre, 2022) 462-481.
EL KASSIMI. F & ZAHI. J (2022). Health insurance pricing using CART decision trees algorithm . International Journal of Computer Engineering and Data Science (IJCEDS), 2(3).
Antwi, S., & Zhao, X. (2012). A logistic regression model for Ghana National Health Insurance claims. International Journal of Business and Social Research, LAR Center Press,vol. 2(7), 139-147.
Astari, D. W., & Kismiantini. (2019). Analysis of Factors Affecting the Health Insurance Ownership with Binary Logistic Regression Model. Journal of Physics : Conf. Series 1320, doi:10.1088/1742-6596/1320/1/012011.
Osman, M. A., & Ismail, E. A. (2018). A Quantitative Model to Identify Key Determinants for Health Insurance Claims in the Kingdom of Saudi Arabia. J. King Saud Univ., Vol. 27, 23-36.
Mounika, K. G., & Deepa, N. (2023). By contrasting decision trees with logistic regression, a novel categorization-based cost prediction method for health insurance may be developed under supervision. Journal of Survey in Fisheries Sciences, 1468-1477.
Jun, J. S. (2020). Identification and Prediction of Factors Impact America Health Insurance Premium. Dublin: Masters thesis, National College of Ireland.
Kaushik, K., Bhardwaj, A., Dhar Dwivedi, A., & Singh, R. (2022). Machine Learning-Based Regression Framework to Predict Health Insurance Premiums. Int. J. Environ. Res. Public Health. doi:doi:org/10.3390/19137898.
Rakotomalala, R. (2011). Pratique de la Régression Logistique. Université Lumière Lyon 2.
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Copyright (c) 2023 Fatima EL KASSIMI
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