Citation:

Jaya Vardhani Mamidala, Sunil Jacob Enokkaren, Avinash Attipalli, Varun Bitkuri, Raghuvaran Kendyala, Jagan Kurma. "Machine Learning Models Powered by Big Data for Health Insurance Expense Forecasting" International Research Journal of Economics and Management Studies, Vol. 2, No. 1, pp. 333-344, 2023.

Abstract:

Health insurance is the critical tool that can be adopted to reinforce the healthcare systems, especially among the low-income groups, and it does so by enhancing health outcomes, productivity and labor supply. Being aware of the cost of healthcare in terms of precise expense projections is important to policymakers and insurance agents. The research suggests a regime of machine learning to estimate health insurance expenses with the publicly available medical insurance cost prediction data hosted on Kaggle. The dataset consists of 2.7k records and their significant attributes such as age, BMI, and whether they smoke or not as well as their region. A thorough preprocessing procedure has been conducted; it includes data cleaning, removal of outliers, one-hot, and Z-score standardization. Gradient Boosting (GB) regression model was also applied in prediction of insurance expenses taking advantage of the ensemble learning behavior, which continuously minimizes errors in predictions. Predictive accurateness was high as the R 2 of the model was 92.0 and the Mean Squared Error (MSE) was given as 86.8. The outcomes confirm that Gradient Boosting is effective in fitting complex and non-linear relations that represent a viable source and mature scale solution to predicting personal healthcare expenditures.

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Keywords:

Health Insurance, Cost Prediction, Machine Learning, Healthcare Analytics, Policy Feedback, Insurance Expenses.