Health insurance risk classification using multinomial logistic regression

Authors

  • Fatima EL KASSIMI University Hassan 1st, Faculty of Economics and Management, LM2CE, Settat, Morocco
  • Ayyoub SAOUDI University Hassan 1st, Faculty of Economics and Management, LM2CE, Settat, Morocco
  • Jamal ZAHI University Hassan 1st, Faculty of Economics and Management, LM2CE, Settat, Morocco

Keywords:

Health insurance, classification, statistical learning, multinomial logistic regression

Abstract

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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References

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Published

2023-08-16

How to Cite

EL KASSIMI, F., SAOUDI, A. ., & ZAHI, J. . (2023). Health insurance risk classification using multinomial logistic regression . International Journal of Computer Engineering and Data Science (IJCEDS), 3(2), 13–18. Retrieved from https://ijceds.com/ijceds/article/view/54