DOI QR코드

DOI QR Code

Wellness Prediction in Diabetes Mellitus Risks Via Machine Learning Classifiers

  • Saravanakumar M, Venkatesh (PG & Research Dept. of Computer Science, Jamal Mohamed College (Autonomous)) ;
  • Sabibullah, M. (PG & Research Dept. of Computer Science, Jamal Mohamed College (Autonomous))
  • Received : 2022.04.05
  • Published : 2022.04.30

Abstract

The occurrence of Type 2 Diabetes Mellitus (T2DM) is hoarding globally. All kinds of Diabetes Mellitus is controlled to disrupt over 415 million grownups worldwide. It was the seventh prime cause of demise widespread with a measured 1.6 million deaths right prompted by diabetes during 2016. Over 90% of diabetes cases are T2DM, with the utmost persons having at smallest one other chronic condition in UK. In valuation of contemporary applications of Big Data (BD) to Diabetes Medicare by sighted its upcoming abilities, it is compulsory to transmit out a bottomless revision over foremost theoretical literatures. The long-term growth in medicine and, in explicit, in the field of "Diabetology", is powerfully encroached to a sequence of differences and inventions. The medical and healthcare data from varied bases like analysis and treatment tactics which assistances healthcare workers to guess the actual perceptions about the development of Diabetes Medicare measures accessible by them. Apache Spark extracts "Resilient Distributed Dataset (RDD)", a vital data structure distributed finished a cluster on machines. Machine Learning (ML) deals a note-worthy method for building elegant and automatic algorithms. ML library involving of communal ML algorithms like Support Vector Classification and Random Forest are investigated in this projected work by using Jupiter Notebook - Python code, where significant quantity of result (Accuracy) is carried out by the models.

Keywords

References

  1. J. Vijayaraj, R. Saravanan, P. Victer Paul and R. Raju, "A comprehensive survey on big data analytics tools," 2016 Online International Conference on Green Engineering and Technologies (IC-GET), 2016, pp. 1-6, doi: 10.1109/GET.2016.7916733
  2. H. Khalajzadeh, M. Abdelrazek, J. Grundy, J. Hosking and Q. He, "A Survey of Current End-User Data Analytics Tool Support," 2018 IEEE International Congress on Big Data (BigData Congress), 2018, pp. 41-48, doi: 10.1109/BigDataCongress.2018.00013.
  3. S. P. Menon and N. P. Hegde, "A survey of tools and applications in big data," 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO), 2015, pp. 1-7, doi: 10.1109/ISCO.2015.7282364.
  4. B. Yadranjiaghdam, N. Pool and N. Tabrizi, "A Survey on Real-Time Big Data Analytics: Applications and Tools," 2016 International Conference on Computational Science and Computational Intelligence (CSCI), 2016, pp. 404-409, doi: 10.1109/CSCI.2016.0083
  5. S. Wadhera, D. Kamra, A. Kumar, A. Jain and V. Jain, "A systematic Review of Big data tools and application for developments," 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), 2021, pp. 561-566, doi: 10.1109/ICIEM51511.2021.9445326.
  6. S. K. Bhatt and Srinivasan, "Survey on Big Data Analytics:Domain Areas and Features," 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2020, pp. 254-258, doi: 10.1109/ICACCCN51052.2020.9362939.
  7. D. P. Acharjya and Kauser Ahmed P, "A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools" International Journal of Advanced Computer Science and Applications(IJACSA), 7(2), 2016. http://dx.doi.org/10.14569/IJACSA.2016.07026
  8. A. Jaiswal and P. Bagale, "A Survey on Big Data in Financial Sector," 2017 International Conference on Networking and Network Applications (NaNA), 2017, pp. 337-340, doi: 10.1109/NaNA.2017.46.
  9. A. Jaiswal, V. K. Dwivedi and O. P. Yadav, "Big Data and its Analyzing Tools : A Perspective," 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 2020, pp. 560-565, doi: 10.1109/ICACCS48705.2020.9074222.
  10. M. Merrouchi, M. Skittou and T. Gadi, "Popular platforms for big data analytics: A survey," 2018 International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), 2018, pp. 1-6, doi: 10.1109/ICECOCS.2018.8610652.
  11. Banchhor, C. O. & Srinivasu, N. (2020). Survey Of Technologies, Tools, Concepts And Issues In Big Data, international journal of scientific & technology research , VOLUME 9, ISSUE 04, pp:1901-1911,APRIL 2020,, 9.0(4.0):1901.0-1911.0.