Interpretable Machine Learning Framework for Personalized Health Insurance Risk Prediction

  • Mohammed Al-Mhadawi
  • Qahtan M. Yas
DOI: https://doi.org/10.36079/lamintang.ijortas-0802.1081
Keywords: Charge Prediction, Explainable AI, Gradient Boosting, Health Insurance Risk, Tabular Data Modeling

Abstract

In modern health insurance systems, accurate medical expenditure prediction is vital for financial solvency and equitable premium distribution. However, deployment is often hampered by the trade-off between predictive accuracy and model transparency. This study presents a unified, interpretable machine learning regression framework to evaluate personalized health insurance charges using structured tabular data. We benchmarked six predictive architectures (five tree ensembles and a multi-layer perceptron control) on a real-world dataset (n=1,338). Experimental results demonstrate that Gradient Boosting achieved superior performance with R2=0.8789, RMSE = $4,335.47, MAPE = 28.49%, and an operational model footprint of only 170 KB. In contrast, standard deep learning (MLP) failed catastrophically (R2 = −0.3947) due to severe right-skewness and nonlinear tabular feature interactions. Model interpretability via SHAP values identified smoker status (48.2% Gini importance) and BMI interactions as primary cost drivers. Future research will focus on evaluating hybrid TabNet-Boosting architectures and integrating longitudinal temporal claims to capture evolving risk profiles across multinational cohorts.

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Author Biographies

Mohammed Al-Mhadawi

General Directorate for Education of Diyala, Ministry of Education. Diyala, Iraq.

Qahtan M. Yas

College of Engineering for Artificial Intelligence Technology, University of Diyala. Baqubah, Iraq.

This is an open access article, licensed under CC-BY-SA

Creative Commons License
Published
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2026-09-07
    Downloads : 4
How to Cite
[1]
M. Al-Mhadawi and Q. M. Yas, “Interpretable Machine Learning Framework for Personalized Health Insurance Risk Prediction”, International Journal of Recent Technology and Applied Science, vol. 8, no. 2, pp. 89-102, Sep. 2026.
Issue
Vol 8 No 2: September 2026
Section
Articles

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