A SMART SOCIAL INSURANCE BIG DATA ANALYTICS FRAMEWORK BASED ON MACHINE LEARNING ALGORITHMS | [Journal of Theoretical and Applied Information Technology • 2020]

Author(s):

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

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

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

4. 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%.

Page(s): 232-244

DOI: DOI not available

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

Keywords:

Data Integration , Big Data Mining and Big Data Analytics

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