ABSTRACT
Health has become the major contribution for the growth of any country. Therefore, Artificial intelligence and Machine learning are the appropriate approaches to make human life easier by anticipating and diagnosing with prediction of the future. Still many researchers are investigating which approach is best for predicting medical insurance. This research uses multiple algorithms to identify which cognitive approach is better for predicting healthcare insurance premium estimation. The proposed research uses PCA for feature extraction and then trains the model by using Linear Regression, Support Vector Regression, Decision Tree, Random Forest Regression, and k-Nearest Neighbors and Artificial neural network are all used in the suggested study methodology. The dataset is acquired from the KAGGLE repository and different machine learning and neural network methods are used to show the analysis and to compare the model accuracy. The results show that the ANN is the best model which gives 96% accuracy for predicting health insurance.
