Design of a Data Analytics Model for National Health Insurance Scheme
DOI:
https://doi.org/10.12856/JHIA-2017-v4-i1-163Abstract
Abstract
Background and Purpose: The need for better and faster decision-making based on data is stronger than ever; and being able to use massive amounts of data is a necessary competitive advantage. This has necessitated the urgent need for a sophisticated data analytics model for the effective transformation of data into actionable information to enhance quality decision-making. For instance, the healthcare domain is faced with unnecessary delays in the processing of the data submitted to the National Health Insurance Scheme (NHIS).
Methods: To address this, a data analytics model based on deep learning was designed in this research using unified modelling language.
Results: This model is intended to be implemented using Apache Hadoop and MySQL. When implemented, the model will make it easier to consolidate, cleanse, analyse, and publish data, so that all stakeholders will get information that they can act on, in the format they need.
Conclusion: Thus, the stakeholders will access the information more easily, which will enable them to plan, evaluate, and collaborate more effectively.
Keywords: Hadoop, Deep Learning, Data Analytics, Health Insurance
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References
Meen V. Data analytics at work: Introducing a Data Mining Process Framework to Enable Consultants to Determine Effective Data Analytics Tasks. Unpublished MSc. Thesis submitted to the Faculty of Technology, Policy and Management Delft University of Technology: 2012.
Hoyt RE, Yoshihashi A. Health Informatics: Practical Guide for Healthcare and Information Technology Professionals Sixth Edition., FL; Pensacola: 2014.
Sun K, Wei X, Jia G, Wang R, Li R. Large-scale Artificial Neural Network: MapReduce-based Deep Learning. NerveCloud in the course of EEL-6935: Cloud Computing and Storage, Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611 USA. 2015; 1-9.
Agba MO, Ushie EM, Osuchukwu NC. National Health Insurance Scheme (NHIS) and Employees’ Access to Healthcare Services in Cross River State, Nigeria. GJHSS. 2010; 10(7): 9-16.
Eteng FO, Ijim-Agbor U. Understanding the challenges and prospects of administering the national health insurance scheme in Nigeria. IJHSSR. 2016; 2(8): 43-48.
Oyegoke TO. Development of an Integrated Health Management System for National Health Insurance Scheme. An Unpublished M.Sc. Thesis Submitted to the Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria: 2015.
Chen CLP, Zhang CY. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. JIS. 2014; 275: 314–347.
Zhang K, Chen X. Large-Scale Deep Belief Nets with MapReduce. IEEEA-PI. 2014; 2: 395-403.
Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E. Deep Learning Applications and Challenges in Big Data Analytics. JBD. 2015; 2(1): 1-21.
Leelavathi MV, SahanaDevi KJ. Efficient Deep Learning for Big Data: A Review. IJCSE. 2016. 4(3), 30-35.
Bagul PD, Bojewar S, Sanghavi A. Survey on Hybrid Approach for Fraud Detection in Health Insurance. IJIRCCE. 2016; 4(4): 6918-6922.
Fashoto SG, Owolabi O, Sadiku J, Gbadeyan JA. Application of Data Mining Technique for Fraud Detection in Health Insurance Scheme Using Knee-Point K-Means Algorithm. AJBAS. 2013; 7(8): 140-144.
Moyano LG, Appel AP, de Santana VF, Ito M, dos Santos TD. GraPhys: Understanding Health Care Insurance Data through Graph Analytics. WWW ’16 Companion, April 11–15, 2016; Montréal, Québec, Canada, 2016. 227-230.
Yunusa U, Irinoye O, Suberu A, Garba AM, Timothy G, Dalhatu A, Ahmed S. Trends and Challenges of Public Health Care Financing System in Nigeria: The Way Forward. IOSR-JEF. 2014; 4(3): 28-34.
