文章基本信息

标题：A SMART SOCIAL INSURANCE BIG DATA ANALYTICS FRAMEWORK BASED ON MACHINE LEARNING ALGORITHMS
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作者：YOUSSEF SENOUSY ; ABDULAZIZ SHEHAB ; ALAA M. RIAD 等
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：2
页码：232-244
出版社：Journal of Theoretical and Applied
摘要：Social insurance is an individual’s protection against risks such as retirement, death or disability. Big data mining and analytics in a way that could help the insurers and the actuaries to get the optimal decision for the insured individuals. Dependently, this paper proposes a novel analytic framework for Egyptian Social insurance big data. NOSI’s data contains data which needs some pre-processing methods after extraction like replacing missing values, standardization and outlier/extreme data. The paper also presents using some mining methods such as clustering and classification algorithms on the Egyptian social insurance dataset through an experiment. In clustering, we used K-means clustering and the result showed a silhouette score 0.138 with two clusters in the dataset features. In classification, we used the Support Vector Machine (SVM) classifier and classification results showed a high accuracy percentage of 94%.
关键词：Social Insurance;Data Integration;Big Data Mining and Big Data Analytics