Skip to main content

Advertisement

Log in

Modified adaptive neuro-fuzzy inference system (M-ANFIS) based multi-disease analysis of healthcare Big Data

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Big Data (BD) has turned into a significant research field owing to the dawn of vast quantity of data generated as of various sources like Internet of things (IoT), social media, and also multimedia applications. BD has played an imperative part in numerous decision-makings as well as forecasting domains for instance health care, recommendation systems, web display advertisement, transportation, clinicians, business analysis, and fraud detection along with tourism marketing. The domain of health care attained its influence by the effect of BD since the data sources concerned in the healthcare organizations are famous for their volume, heterogeneous complexity, and high dynamism. Though the function of BD analytical techniques, platforms, and tools are realized among various domains, their effect on healthcare organization for possible healthcare applications shows propitious research directions. This paper concentrates on the analysis of multiple diseases using modified adaptive neuro-fuzzy inference system (M-ANFIS). Initially, the healthcare BD undergoes pre-processing. In the pre-processing step, data format identification and integration of the healthcare BD dataset is done. Now, features are extracted from the preprocessed dataset and the count of the closed frequent item set (CFI) is found. Then, the entropy of the CFI count is determined. Finally, analyses of the multiple diseases are executed with the aid of M-ANFIS. In M-ANFIS, k-medoid clustering is used to cluster the CFI entropy of healthcare BD. The proposed method’s performance is assessed by comparing it with the other existent techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Shafqat S, Kishwer S, Rasool RU, Qadir J, Amjad T, Ahmad HF (2018) Big data analytics enhanced healthcare systems: a review. J Supercomput. https://doi.org/10.1007/s11227-017-2222-4

    Article  Google Scholar 

  2. Grover P, Kar AK (2017) Big data analytics: a review on theoretical contributions and tools used in literature. Glob J Flex Syst Manag 18(3):203–229

    Article  Google Scholar 

  3. Mohamed A, Najafabadi MK, Wah YB, Zaman EAK, Maskat R (2019) The state of the art and taxonomy of big data analytics: view from new big data framework. Artif Intell Rev. https://doi.org/10.1007/s10462-019-09685-9

    Article  Google Scholar 

  4. Garattini C, Raffle J, Aisyah DN, Sartain F, Kozlakidis Z (2017) Big data analytics, infectious diseases and associated ethical impacts. Philos Technol 32:1–17

    Google Scholar 

  5. Soleimani-Roozbahani F, Ghatari AR, Radfar R (2019) Knowledge discovery from a more than a decade studies on healthcare Big Data systems: a scientometrics study. J Big Data 6(1):8

    Article  Google Scholar 

  6. Chen M, Hao Y, Hwang K, Wang L, Wang L (2017) Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5:8869–8879

    Article  Google Scholar 

  7. Jagadeeswari V, Subramaniyaswamy V, Logesh R, Vijayakumar V (2018) A study on medical internet of things and big data in personalized healthcare system. Health Inf Sci Syst 6(1):14

    Article  Google Scholar 

  8. Alonso SG, de la Torre Díez I, Rodrigues JJ, Hamrioui S, López-Coronado M (2017) A systematic review of techniques and sources of big data in the healthcare sector. J Med Syst 41(11):183

    Article  Google Scholar 

  9. Malik MM, Abdallah S, Ala’raj M (2018) Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review. Ann Oper Res 270(1–2):287–312

    Article  MathSciNet  Google Scholar 

  10. Razzak MI, Imran M, Xu G (2019) Big data analytics for preventive medicine. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04095-y

    Article  Google Scholar 

  11. Rao TR, Mitra P, Bhatt R, Goswami A (2018) The big data system, components, tools, and technologies: a survey. Knowl Inf Syst 3:1–81

    Google Scholar 

  12. Kashyap H, Ahmed HA, Hoque N, Roy S, Bhattacharyya DK (2016) Big data analytics in bioinformatics: architectures, techniques, tools and issues. Netw Model Anal Health Inf Bioinf 5(1):28

    Article  Google Scholar 

  13. Bikash Kanti Sarkar (2017) Big data for secure healthcare system: a conceptual design. Complex Intell Syst 3(2):133–151

    Article  Google Scholar 

  14. Austin Christopher, Kusumoto Fred (2016) The application of big data in medicine: current implications and future directions. J Interv Card Electrophysiol 47(1):51–59

    Article  Google Scholar 

  15. AlFarraj O, AlZubi A, Tolba A (2019) Optimized feature selection algorithm based on fireflies with gravitational ant colony algorithm for big data predictive analytics. Neural Comput Appl 31(5):1391–1403

    Article  Google Scholar 

  16. Hu Y, Duan K, Zhang Y, Hossain MS, Rahman SMM, Alelaiwi A (2018) Simultaneously aided diagnosis model for outpatient departments via healthcare big data analytics. Multimed Tools Appl 77(3):3729–3743

    Article  Google Scholar 

  17. Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar R, Thota C (2018) A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Gener Comput Syst 82:375–387

    Article  Google Scholar 

  18. Nair Lekha R, Shetty Sujala D, Shetty Siddhanth D (2018) Applying spark based machine learning model on streaming big data for health status prediction. Comput Electr Eng 65:393–399

    Article  Google Scholar 

  19. Kalid N, Zaidan AA, Zaidan BB, Salman OH, Hashim M, Muzammil H (2018) Based real time remote health monitoring systems: a review on patients prioritization and related” big data” using body sensors information and communication technology. J Med Syst 42(2):30

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. Vidhya.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vidhya, K., Shanmugalakshmi, R. Modified adaptive neuro-fuzzy inference system (M-ANFIS) based multi-disease analysis of healthcare Big Data. J Supercomput 76, 8657–8678 (2020). https://doi.org/10.1007/s11227-019-03132-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-019-03132-w

Keywords

Navigation