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All Journal International Journal of Recent Technology and Applied Science (IJORTAS)
Mohammed Al-Mhadawi
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Interpretable Machine Learning Framework for Personalized Health Insurance Risk Prediction Mohammed Al-Mhadawi; Qahtan M. Yas
International Journal of Recent Technology and Applied Science (IJORTAS) Vol 8 No 2: September 2026
Publisher : Lamintang Education and Training (LET) Centre

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijortas-0802.1081

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.
Co-Authors Qahtan M. Yas