Machine Learning Approach for Calculating Healthcare Protection Premiums

Abstract

Healthcare insurance plays a vital role in reducing the financial burden associated with medical treatments and improving access to quality healthcare services. Determining an appropriate healthcare protection premium is a challenging task because it depends on multiple demographic, medical, and lifestyle factors that influence an individual\'s health risk. Conventional premium calculation methods generally rely on actuarial models and statistical techniques, which often fail to capture the complex nonlinear relationships among these variables. Consequently, insurance providers may encounter inaccurate premium estimation, affecting both customer satisfaction and financial stability. This research proposes a machine learning-based framework for predicting healthcare protection premiums using Multiple Linear Regression (MLR), Random Forest Regressor (RFR), and Support Vector Regression (SVR). The proposed approach utilizes historical healthcare insurance data to identify patterns and relationships among customer attributes such as age, gender, Body Mass Index (BMI), smoking habits, medical history, policy duration, and family size. Data preprocessing techniques, including handling missing values, feature encoding, and normalization, are applied before model training. The models are evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R² Score). Comparative analysis indicates that the Random Forest Regressor provides the most accurate and reliable premium predictions. The proposed system improves premium estimation accuracy, reduces manual effort, supports personalized pricing, and enhances decision-making for healthcare insurance providers.

Introduction

Healthcare insurance plays a crucial role in protecting individuals from rising medical expenses, making accurate premium estimation essential for both insurers and policyholders. Traditional premium calculation methods rely on actuarial science and statistical models, which often assume linear relationships among risk factors and fail to capture complex interactions between demographic, medical, and lifestyle variables. This can result in inaccurate premium estimates, leading to financial losses for insurance companies or unfair pricing for customers.

To overcome these limitations, this study proposes a machine learning-based healthcare premium prediction framework using Multiple Linear Regression (MLR), Random Forest Regressor (RFR), and Support Vector Regression (SVR). The system processes healthcare insurance data through data collection, preprocessing, feature selection, model training, and performance evaluation to identify the most accurate model for personalized premium prediction. By learning patterns from historical insurance records, the framework aims to improve prediction accuracy, reduce manual effort, enhance operational efficiency, and support intelligent decision-making.

The literature review highlights that machine learning techniques outperform traditional actuarial methods in premium prediction by improving risk assessment and reducing prediction errors. Ensemble models such as Random Forest and Gradient Boosting have shown superior performance, while Multiple Linear Regression remains a useful baseline because of its simplicity and interpretability.

The study identifies several limitations of existing systems, including their inability to model nonlinear relationships, reliance on manual risk assessment, limited personalization, lack of interpretability, and inaccurate premium estimation. Although some modern systems employ deep learning, these require large datasets, significant computational resources, and often lack transparency.

The proposed system analyzes customer attributes such as age, gender, Body Mass Index (BMI), smoking status, diabetes, hypertension, heart disease, policy duration, and number of insured family members. After preprocessing the data by handling missing values, removing duplicates, encoding categorical variables, and normalizing numerical features, the dataset is divided into training and testing sets. The three regression models are trained and evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² Score. The best-performing model is then used to predict healthcare insurance premiums for new customers.

The primary objectives of the research are to develop an intelligent premium prediction framework, improve prediction accuracy over conventional methods, analyze the impact of demographic, medical, and lifestyle factors, compare the performance of MLR, RFR, and SVR, automate premium estimation, and provide fair, transparent, and personalized pricing recommendations.

The methodology follows five stages: data collection, data preprocessing, model training, model evaluation, and premium prediction, while the overall system design integrates these modules to automate healthcare premium estimation. Overall, the proposed machine learning framework offers a faster, more accurate, transparent, and personalized approach to healthcare insurance premium prediction, benefiting both insurance providers and policyholders.

Conclusion

Healthcare insurance premium estimation is a critical process that directly influences both insurance providers and policyholders. Traditional premium calculation methods often fail to accurately model the complex relationships among demographic, medical, and lifestyle factors, leading to inaccurate premium estimation and inefficient risk assessment. This research proposed a machine learning-based framework for calculating healthcare protection premiums using Multiple Linear Regression, Random Forest Regressor, and Support Vector Regression. The proposed methodology incorporated data preprocessing, feature encoding, model training, and performance evaluation to generate reliable premium predictions. Comparative analysis indicated that the Random Forest Regressor achieved the highest predictive performance due to its ability to capture nonlinear relationships while minimizing prediction errors. The proposed system improves prediction accuracy, reduces manual effort, supports personalized insurance pricing, and enhances operational efficiency. It provides a scalable and reliable solution that can assist healthcare insurance organizations in implementing fair, transparent, and data-driven premium estimation.

References

[1] R. Girshick, J. Donahue, T. Darrell, and J. Malik, \"Rich feature hierarchies for accurate object detection and semantic segmentation,\" in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, 2014, pp. 580-587. [2] R. Girshick, \"Fast R-CNN,\" in Proc. IEEE Int. Conf. Computer Vision (ICCV), Santiago, Chile, 2015, pp. 1440-1448. [3] S. Ren, K. He, R. Girshick, and J. Sun, \"Faster R-CNN: Towards real-time object detection with region proposal networks,\" IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137-1149, Jun. 2017. [4] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, \"You only look once: Unified, real-time object detection,\" in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 779-788. [5] J. Redmon and A. Farhadi, \"YOLO9000: Better, faster, stronger,\" in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 6517-6525. [6] J. Redmon and A. Farhadi, \"YOLOv3: An incremental improvement,\" arXiv preprint arXiv:1804.02767, 2018. [7] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, \"YOLOv4: Optimal speed and accuracy of object detection,\" arXiv preprint arXiv:2004.10934, 2020. [8] C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, \"YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,\" in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, 2023, pp. 7464-7475. [9] G. Jocher, A. Chaurasia, and J. Qiu, \"Ultralytics YOLOv8,\" Ultralytics, 2023. [Online]. Available: https://github.com/ultralytics/ultralytics [10] W. Liu et al., \"SSD: Single shot multibox detector,\" in Proc. European Conf. Computer Vision (ECCV), Amsterdam, Netherlands, 2016, pp. 21-37. [11] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, \"Focal loss for dense object detection,\" in Proc. IEEE Int. Conf. Computer Vision (ICCV), Venice, Italy, 2017, pp. 2980-2988. [12] T.-Y. Lin et al., \"Feature pyramid networks for object detection,\" in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 2117-2125. [13] N. Carion et al., \"End-to-end object detection with transformers,\" in Proc. European Conf. Computer Vision (ECCV), Glasgow, UK, 2020, pp. 213-229. [14] K. He, G. Gkioxari, P. Dollar, and R. Girshick, \"Mask R-CNN,\" in Proc. IEEE Int. Conf. Computer Vision (ICCV), Venice, Italy, 2017, pp. 2961-2969. [15] A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, \"Simple online and realtime tracking,\" in Proc. IEEE Int. Conf. Image Processing (ICIP), Phoenix, AZ, USA, 2016, pp. 3464-3468. [16] N. Wojke, A. Bewley, and D. Paulus, \"Simple online and realtime tracking with a deep association metric,\" in Proc. IEEE Int. Conf. Image Processing (ICIP), Beijing, China, 2017, pp. 3645-3649. [17] Y. Zhang et al., \"ByteTrack: Multi-object tracking by associating every detection box,\" in Proc. European Conf. Computer Vision (ECCV), Tel Aviv, Israel, 2022, pp. 1-21. [18] [18] R. E. Kalman, \"A new approach to linear filtering and prediction problems,\" Trans. ASME J. Basic Eng., vol. 82, no. 1, pp. 35-45, 1960. [19] H. W. Kuhn, \"The Hungarian method for the assignment problem,\" Naval Research Logistics Quarterly, vol. 2, no. 1-2, pp. 83-97, 1955. [20] N. Dalal and B. Triggs, \"Histograms of oriented gradients for human detection,\" in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA, 2005, pp. 886-893. [21] D. G. Lowe, \"Distinctive image features from scale-invariant keypoints,\" Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004. [22] P. Viola and M. Jones, \"Rapid object detection using a boosted cascade of simple features,\" in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Kauai, HI, USA, 2001, pp. 511-518. [23] O. Barnich and M. Van Droogenbroeck, \"ViBe: A universal background subtraction algorithm for video sequences,\" IEEE Trans. Image Process., vol. 20, no. 6, pp. 1709-1724, Jun. 2011. [24] G. Bradski, \"The OpenCV library,\" Dr. Dobb\'s Journal of Software Tools, vol. 25, no. 11, pp. 120-125, 2000. [25] A. Krizhevsky, I. Sutskever, and G. E. Hinton, \"ImageNet classification with deep convolutional neural networks,\" in Proc. Advances in Neural Information Processing Systems (NeurIPS), Lake Tahoe, NV, USA, 2012, pp. 1097-1105. [26] K. He, X. Zhang, S. Ren, and J. Sun, \"Deep residual learning for image recognition,\" in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770-778. [27] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, \"The PASCAL Visual Object Classes (VOC) challenge,\" Int. J. Comput. Vis., vol. 88, no. 2, pp. 303-338, 2010. [28] T.-Y. Lin et al., \"Microsoft COCO: Common objects in context,\" in Proc. European Conf. Computer Vision (ECCV), Zurich, Switzerland, 2014, pp. 740-755. [29] S. Ioffe and C. Szegedy, \"Batch normalization: Accelerating deep network training by reducing internal covariate shift,\" in Proc. Int. Conf. Machine Learning (ICML), Lille, France, 2015, pp. 448-456. [30] D. P. Kingma and J. Ba, \"Adam: A method for stochastic optimization,\" in Proc. Int. Conf. Learning Representations (ICLR), San Diego, CA, USA, 2015, pp. 1-15.

Copyright

Copyright © 2026 D. Rishika, Tulasi Miryala. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.