Abstract
The health insurance is an important big eye-openers during the emergency need during accidents and disease pandemic situations. Many of the people will lag to hit financially and to bear the operational expenses during treatment. the need for health insurance changes from youth to old age depending on your lifestyle and genetics. Due to the change in lifestyle and diseases, the health insurance is much needed for each individual. Since it is uncertain that a medical emergency can attack anyone, anytime that impact the person so emotionally and financially. With all this background, this paper attempts to predict the Health cost insurance based on the accessible parameters like age, sex, region, Smoking, Body Mass Index, Children with the following contributions. Firstly, the Health Cost Insurance dataset is extracted from UCI machine repository and the data is preprocessed along with exploratory data analysis. Secondly, the anova test is applied to verify the features with Probability of F-Statistic PR(>F) < 0.05 that highly influence the Target. Thirdly, the raw dataset and the feature scaled dataset is applied to all the Linear Regression models and the performance is analyzed. Fourth, the raw dataset and the feature scaled dataset is applied to all the Ensembling Regression models and the performance is analyzed through intercept, MAE, MSE, R2Score, and EVS. Anova Test Reults shows that the variable ‘region’ does not influence the target as the F-statistic value is 0.14. Experimental results show that polynomial regression is achieving 88% of R2Score before and after feature scaling. The Random Forest regression is achieving 86% of R2Score before and after feature scaling.
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Shyamala Devi, M. et al. (2021). Linear and Ensembling Regression Based Health Cost Insurance Prediction Using Machine Learning. In: Satapathy, S.C., Bhateja, V., Favorskaya, M.N., Adilakshmi, T. (eds) Smart Computing Techniques and Applications. Smart Innovation, Systems and Technologies, vol 224. Springer, Singapore. https://doi.org/10.1007/978-981-16-1502-3_49
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DOI: https://doi.org/10.1007/978-981-16-1502-3_49
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