Reports on Machine Learning Findings from Lviv Polytechnic National University Provide New Insights (An Ensemble Methods for Medical Insurance Costs Prediction Task): Machine Learning

2022 JAN 04 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Data detailed on Machine Learning have been presented. According to news reporting out of Lvov, Ukraine, by NewsRx editors, research stated, “The paper reports three new ensembles of supervised learning pre-dictors for managing medical insurance costs. The open dataset is used for data analysis methods development.”

Financial support for this research came from National Research Foundation of Ukraine.

Our news journalists obtained a quote from the research from Lviv Polytechnic National University, “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.”

According to the news editors, the research concluded: “The quality of the developed ensemble for Root Mean Squared Error metric is 1.47 better than for the best weak predictor (SVR).”

This research has been peer-reviewed.

For more information on this research see: An Ensemble Methods for Medical Insurance Costs Prediction Task. Computers, Materials & Continua, 2022;70(2):3969-3984. Computers, Materials & Continua can be contacted at: Tech Science Press, 871 Coronado Center Dr, Sute 200, Henderson, NV 89052, USA.

Our news journalists report that additional information may be obtained by contacting Nataliia Melnykova, Lviv Polytechnic National University, Dept. of Artificial Intelligence, Ua-79013 Lvov, Ukraine. Additional authors for this research include Nataliya Shakhovska, Valentyna Chopiyak and Michal Gregus Ml.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.32604/cmc.2022.019882. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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