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 ; Wael K. Hanna 等
期刊名称：Cybernetics and Information Technologies
印刷版ISSN：1311-9702
电子版ISSN：1314-4081
出版年度：2020
卷号：20
期号：1
页码：95-111
语种：English
出版社：Bulgarian Academy of Science
摘要：Social insurance is an individual’s protection against risks such asretirement, death or disability. Big data mining and analytics are a way that couldhelp the insurers and the actuaries to get the optimal decision for the insuredindividuals. Dependently, this paper proposes a novel analytic framework forEgyptian Social insurance big data. NOSI’s data contains data, which need somepre-processing methods after extraction like replacing missing values,standardization and outlier/extreme data. The paper also presents using some miningmethods, such as clustering and classification algorithms on the Egyptian socialinsurance dataset through an experiment. In clustering, we used K-means clusteringand the result showed a silhouette score 0.138 with two clusters in the datasetfeatures. In classification, we used the Support Vector Machine (SVM) classifier andclassification results showed a high accuracy percentage of 94%.
关键词：Social Insurance; Data Integration; Big Data Mining ; Big Data Analytics