A SMART SOCIAL INSURANCE BIG DATA ANALYTICS FRAMEWORK BASED ON MACHINE LEARNING ALGORITHMS

YOUSSEF SENOUSY; ABDULAZIZ SHEHAB; ALAA M. RIAD; NASHAAT ELKHAMISY

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A SMART SOCIAL INSURANCE BIG DATA ANALYTICS FRAMEWORK BASED ON MACHINE LEARNING ALGORITHMS

Author(s)

YOUSSEF SENOUSY Department of Computers and Information Systems, Mansoura University, Mansoura, Egypt

ABDULAZIZ SHEHAB Department of Computer Science, College of Science and Arts, Jouf University,KSA; Department of Computers and Information Systems, Mansoura University, Mansoura,Egypt

ALAA M. RIAD Department of Computers and Information Systems, Mansoura University, Mansoura, Egypt

NASHAAT ELKHAMISY Department of Information Systems, Sadat Academy for Management Sciences, Cairo, Egypt

Abstract

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%.

Publication Details

Page(s) 232-244

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 98, Issue: 2, Year: 2020

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Keywords

Data Integration, Big Data Mining and Big Data Analytics

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