期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2020
卷号：98
期号：22
页码：3428-3437
出版社：Journal of Theoretical and Applied
摘要：One of the main challenges facing the insurance companies is to determine the proper insurance premium for each risk represented by customers. Risk differs widely from clients to another, and a Careful understanding of various risk factors assists predict the likelihood of insurance claims based on historical data, Real-world datasets often have missing values, can cause bias in results. the most widely adopted methods for dealing with missing data is to remove observations having missing values, perform a complete case analysis (CCA) and single imputation such as average. these approaches have the disadvantages represent in loss of precision and biased. The main objective of the paper is to build a precise model to predict car insurance claims through machine learning techniques. with a focus on advanced statistical methods and machine learning algorithms that are the most suitable method for handling missing values. we Used available datasets through Kaggle which consists of 12 variables and 30240 cases, the research was carried out by using Artificial Neural Network (ANN), Decision Tree (DT), Na�ve Bayes classifiers, and XGBoost to develop the prediction model. The experimental results showed that the model obtained acceptable results The XGBoost model and Resolution Tree achieved the best accuracy among the four models, with an accuracy of 92.53% and 92.22%, respectively.
