A Schematic View of the Application of Big Data Analytics in Healthcare Crime Investigation


  • Terungwa Simon Yange Federal University of Agriculture, Makurdi, Nigeria
  • Hettie Abimbola Soriyan Obafemi Awolowo University, Ile-Ife
  • O Oluoha University of Nigeria Nsukka, Nigeria





Background and Purpose: One major challenge encountered during crime investigation via automated systems is the inability of conventional data analysis techniques to adequately handle the enormous data that are made available during the investigation. Existing crime investigation frameworks are built on orthodox data analysis techniques which cannot sufficiently manned the unprecedented size and variety of data available today, not to mention the significantly more anticipated data in the near future. This has affected the healthcare industry where data is predominantly multi-structured and is growing at a considerably faster rate.

Methods: To address this, a big data analytics model based on deep learning was designed in this research using enterprise application diagrams.

Results: This model is intended to be implemented using Apache Hadoop a big data implementation framework. When implemented, the model will create a platform that will handle a phenomenon that is affecting millions of people all over the world.

Conclusions: This is the first of its kind to use big data analytics techniques in healthcare crime investigation in Nigeria which provided security intelligence by shortening the time of correlating and deriving evidence from large volume of data for healthcare crime investigation purposes. Finally, this research also enabled the healthcare systems to systematically use big data analytics to identify inefficiencies and best practices that improve care delivery and reduce costs.

Keywords: Crime, Hadoop, Deep Learning, Investigation Data Analytics, Health Insurance


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Ularu EU, Puican FC, Apostu A, Velicanu M. Perspectives on Big Data and Big Data Analytics. DSJ. 2012’ 3(4): 3-14.

IACA (International Association of Crime Analysts). Definition and Types of Crime Analysis. White Paper released by Standards, Methods, & Technology (SMT) Committee. 2014.

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.

Dutta A, Hongoro C. Scaling Up National Health Insurance in Nigeria: Learning from Case Studies of India, Colombia, and Thailand. Washington, DC: Futures Group, Health Policy Project. 2013.

Dora P, Sekharan GH. Healthcare Insurance Fraud Detection Leveraging Big Data Analytics. IJSR. 2015; 4(4): 2073-2076.

Li J, Huang K-Y, Jin J, Shi J. A survey on statistical methods for health care fraud detection. HCMS. 2008; 11: 275-287.

Ekin T, Ieva F, Ruggeri F, Soyer R. Applications of bayesian methods in detection of healthcare frauds. CET. 2013; 33: 151-156.

Bagul PD, Bojewar S, Sanghavi A. Survey on Hybrid Approach for Fraud Detection in Health Insurance. IJIRCCE. 2016; 4(4): 6918-6922.

Bagde PR, Chaudhari MS. Analysis of Fraud Detection Mechanism in Health Insurance Using Statistical Data Mining Techniques, IJCSIT. 2016; 7(2): 925-927.

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.

Jacqulin MJ, Shrijina S. Implementation of Data Mining in Medical Fraud Detection. IJCA. 2013; 69(5): 1-4.

Musal R. Two models to investigate Medicare fraud within unsupervised databases. ESA. 2010; 37(12): 8628-8633.

Travaille P, Thornton D, Müller RM, Hillegersberg J. Electronic Fraud Detection in the U.S. Medicaid Healthcare Program: Lessons Learned from other Industries. Proceedings of the Seventeenth Americas Conference on Information Systems, Detroit, Michigan August 4th-7th 2011.

Bologa A, Bologa R, Florea A. Big Data and Specific Analysis Methods for Insurance Fraud Detection. DSJ. 2010; 1(1): 30-39.

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.

Konasani V, Biswas M, Koleth PK. Healthcare Fraud Management using Big Data Analytics. An Unpublished Report by Trendwise Analytics, Bangalore, India. 2012.

Rawte V, Anuradha G. Fraud Detection in Health Insurance using Data Mining Techniques. International Conference on Communication, Information & Computing Technology Jan. 16-17, 2015.

Joudaki H, Rashidian A, Minaei-Bidgoli B, Mahmoodi M, Geraili B, Mahdi Nasiri M. Using Data Mining to Detect Health Care Fraud and Abuse. GJHS. 2015; 7(1).

Woz´niak M, Grana M, Corchado E. A survey of multiple classifier systems as hybrid systems. IF. 2014; 16: 3–17.



How to Cite

Yange, T. S., Soriyan, H. A., & Oluoha, O. (2017). A Schematic View of the Application of Big Data Analytics in Healthcare Crime Investigation. Journal of Health Informatics in Africa, 4(1). https://doi.org/10.12856/JHIA-2017-v4-i1-161