An Efficient Framework for Predict Medical Insurance Costs Using Machine Learning | ||||
| Journal of Computing and Communication | ||||
| Article 5, Volume 3, Issue 2 - Serial Number 1, July 2024, Page 55-64 PDF (890.38 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jocc.2024.380150 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Diaa s AbdElminaam   1; Maged Farouk2; Nashwa Shaker2; Omnia Elrashidy2; Reda Elazab2
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| 1Department of Data Science , Faculty of Computer Science , Misr International University , Cairo , Egypt | ||||
| 2Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt | ||||
| Abstract | ||||
| One of the applications of machine learning in predicting medical insurance prices considering health and economic factors is because this branch analyzes how healthcare resources are allocated and how healthcare outcomes are determined. The production of medical insurance prices encounters challenges rooted in data accuracy and ethical consideration of machine learning models. In this paper, we proposed an efficient framework for predicting medical insurance prices and a delicate balance between accuracy and fairness to ensure the efficacy and ethical soundness of the pricing process using five machine learning algorithms MAPE , R2. On four different datasets, Cross-validation number of folder:5 and the best result on MAPE is a tree with the smallest number of errors was 3.9%, Cross-validation number of folder:10 and the best result on MAPE is a tree with the smallest number of errors was 3.5%, Random sampling training set size 80% and testing 20% the best result on MAPE is a tree with the smallest number of errors was 4.1%,%, Random sampling training set size 90% and testing 10% the best result on MAPE is Tree with the smallest number of errors was 4%. The best result of all datasets on MAPE is Tree. | ||||
| Keywords | ||||
| Medical Insurance prediction; Prices of insurances; Machine learning; Tree; Linear regression | ||||
| References | ||||
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[1] Sloan, F. A., & Hsieh, C. R. (2017). Health economics. MIT Press.
[2] ul Hassan, C. A., Iqbal, J., Hussain, S., AlSalman, H., Mosleh, M. A., & Sajid Ullah, S. (2021). A computational intelligence approach for predicting medical insurance cost. Mathematical Problems in Engineering, 2021, 1-13.
[3] Orji, U., & Ukwandu, E. (2024). Machine learning for an explainable cost prediction of medical insurance. Machine Learning with Applications, 15, 100516.
[4] Orji, U., & Ukwandu, E. (2024). Machine learning for an explainable cost prediction of medical insurance. Machine Learning with Applications, 15, 100516.
[5] ul Hassan, C. A., Iqbal, J., Hussain, S., AlSalman, H., Mosleh, M. A., & Sajid Ullah, S. (2021). A computational intelligence approach for predicting medical insurance cost. Mathematical Problems in Engineering, 2021, 1-13.
[6] Kaushik, K., Bhardwaj, A., Dwivedi, A. D., & Singh, R. (2022). Machine learning-based regression framework to predict health insurance premiums. International Journal of Environmental Research and Public Health, 19(13), 7898.
[7] Boodhun, N.; Jayabalan, M. Risk Prediction in Life Insurance Industry Using Supervised Learning Algorithms. Complex Intell. Syst. 2018, 4, 145–154. [Google Scholar] [CrossRef] [Green Version]
[8] Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11).
[9] Dongare, A. D., Kharde, R. R., & Kachare, A. D. (2012). Introduction to artificial neural network. International Journal of Engineering and Innovative Technology (IJEIT), 2(1), 189-194.
[10] Rigatti, S. J. (2017). Random forest. Journal of Insurance Medicine, 47(1), 31-39.
[11] Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
[12] Huang, S., Cai, N., Pacheco, P. P., Narrandes, S., Wang, Y., & Xu, W. (2018). Applications of support vector machine (SVM) learning in cancer genomics. Cancer genomics & proteomics, 15(1), 41-51.
[13] Noble, W. S. (2006). What is a support vector machine?. Nature biotechnology, 24(12), 1565-1567.
[14] Peterson, L. E. (2009). K-nearest neighbor. Scholarpedia, 4(2), 1883.
[15] Kramer, O., & Kramer, O. (2013). K-nearest neighbors. Dimensionality reduction with unsupervised nearest neighbors, 13-23.
[16] Weisberg, S. (2005). Applied linear regression (Vol. 528). John Wiley & Sons
[17] Groß, J. (2003). Linear regression (Vol. 175). Springer Science & Business Media.
[18] Song, Y. Y., & Ying, L. U. (2015). Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 27(2), 130.
[19] Suthaharan, S., & Suthaharan, S. (2016). Decision tree learning. Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning, 237-269.
[20] Su, J., & Zhang, H. (2006, July). A fast decision tree learning algorithm. In Aaai (Vol. 6, pp. 500-505). | ||||
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