Abstract
The paper reports three new ensembles of supervised learning predictors for managing medical insurance costs. The open dataset is used for data analysis methods development. The usage of artificial intelligence in the management of financial risks will facilitate economic wear time and money and protect patients’ health. Machine learning is associated with many expectations, but its quality is determined by choosing a good algorithm and the proper steps to plan, develop, and implement the model. The paper aims to develop three new ensembles for individual insurance costs prediction to provide high prediction accuracy. Pierson coefficient and Boruta algorithm are used for feature selection. The boosting, stacking, and bagging ensembles are built. A comparison with existing machine learning algorithms is given. Boosting modes based on regression tree and stochastic gradient descent is built. Bagged CART and Random Forest algorithms are proposed. The boosting and stacking ensembles shown better accuracy than bagging. The tuning parameters for boosting do not allow to decrease the RMSE too. So, bagging shows its weakness in generalizing the prediction. The stacking is developed using K Nearest Neighbors (KNN), Support Vector Machine (SVM), Regression Tree, Linear Regression, Stochastic Gradient Boosting. The random forest (RF) algorithm is used to combine the predictions. One hundred trees are built for RF. Root Mean Square Error (RMSE) has lifted the to 3173.213 in comparison with other predictors. The quality of the developed ensemble for Root Mean Squared Error metric is 1.47 better than for the best weak predictor (SVR).
Highlights
Digital health is a sector that is growing globally
The highest model accuracy is for stacking developed as a combination of weak predictors
The simulation of the developed method for solving the medical insurance costs prediction task showed a significant increase in accuracy compared with existing approaches
Summary
Digital health is a sector that is growing globally. In the whole world, the number of DigitalHealth companies has been doubled in the last five years [1]. Digital health is a sector that is growing globally. Health companies has been doubled in the last five years [1]. The governments pledged hundreds of millions of dollars to support the local digital health industry. Data science technologies are becoming increasingly used in various fields of human activity. Such active implementation is due to the problems that can be solved using existing solutions in this scientific field. The field of medicine is an excellent platform for implementing innovative approaches to data science. Since digital health is currently a trendy field of medicine, many governments in developed countries invest heavily in its development
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