• Deep Kumar Sahu
• Pushpalata Verma
• N Dutta Swaroop

A medical emergency can be unpredictable and its impact is more than enough to disrupt the biopsychosocial factors which include economic, physical and psychological conditions. In this case, Health insurance prevents a person from going into a situation of poverty by covering the major medical expenses like Treatment costs, Hospital charges, Medical equipment expenses, etc. This research deals with the prediction of health insurance premium pricing using various machine learning methods. The goal is to do a comparative study on which algorithm is the best for predicting insurance premiums, as well as making the most accurate model for the same. The data is taken from the Kaggle containing features such as age, gender, Body-Mass Index, number of children, charges, smokers and region. 10 Machine Learning algorithms are used from linear regression to ensemble methods to the neural network, to find the best possible algorithm for the above task. At the end of the study, the best method is found to be AdaBoost with the Root Mean Square Error 149.61095379400695.

Health insurance, Premium pricing, AdaBoost, Linear regression, Neural network, Random Forest, Decision Tree, Support Vector Regressor.

"Advance analysis & Comparative study on Machine Learning methods for Predicting Health Insurance Premium Pricing ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.g678-g684, November-2024, Available :http://www.jetir.org/papers/JETIR2411672.pdf

"Advance analysis & Comparative study on Machine Learning methods for Predicting Health Insurance Premium Pricing ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppg678-g684, November-2024, Available at : http://www.jetir.org/papers/JETIR2411672.pdf