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An Empirical Study on Diabetes Mellitus Prediction Using Apriori Algorithm

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International Conference on Innovative Computing and Communications

Abstract

Diabetes Mellitus introduce various diseases that affect the way of using sugar in human body. Sugar plays a vital role as it is the main source of energy for cells that build up muscles and tissues. So, any issue that causes the problem to maintain normal blood sugar in our blood can create serious problems. Diabetes is one of the diseases which results in abnormal sugar level in the blood and can occur due to several problems like bad diet, obesity, hypertension, increasing age, depression, etc. Diabetes can lead to cardiovascular disease, kidney, brain, foot, skin, nerve, hearing impairment and eye damage. From this thinking, in this study, we have tried to build up some rules using Association Rule Mining technique with various diabetes symptoms and factors to predict diabetes efficiently. We have got 8 rules using Apriori Algorithm.

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References

  1. A. Bhatia, Y. Chiu (David Chiu), Machine Learning with R Cookbook, 2nd edn. Livery Place 35 Livery Street Birmingham B3 2PB, UK.: Packt (2015). Diabetes, World Health Organization (2017). [Online]. http://www.who.int/news-room/fact-sheets/detail/diabetes. Accessed 25 Jan 2019

  2. G. Govindarajan, J. Sowers, C. Stump, Hypertension and diabetes mellitus. European Cardiovascular Disease (2006)

    Google Scholar 

  3. IDF SEA members, The International Diabetes Federation (IDF), Online (2013). http://www.idf.org/our-network/regions-members/south-east-asia/members/93-bangladesh.html. Accessed 01 Feb 2019

  4. V. Balpande, R. Wajgi, Prediction and severity estimation of diabetes using data mining technique, in 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, India (2017), pp. 576–580

    Google Scholar 

  5. B. Shivakumar, S. Alby, A survey on data-mining technologies for prediction and diagnosis of diabetes, in 2014 International Conference on Intelligent Computing Applications, Coimbatore, India (2014), pp. 167–173

    Google Scholar 

  6. B. Patil, R. Joshi, D. Toshniwal, Association rule for classification of type-2 diabetic patients, in 2010 Second International Conference on Machine Learning and Computing, Bangalore, India (2010), pp. 330–334

    Google Scholar 

  7. G. Simon, P. Caraballo, T. Therneau, S. Cha, M. Castro, P. Li, Extending association rule summarization techniques to assess risk of diabetes mellitus, in IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 1, pp. 130–141 (2015). Accessed 12 Feb 2019

    Google Scholar 

  8. P.H. Khotimah, A. Hamasaki, M. Yoshikawa, O. Sugiyama, K. Okamoto, T. Kuroda, On association rule mining from diabetes medical history, in DEIM (2018), pp. 1–5

    Google Scholar 

  9. C. Raveendra, M. Thiyagarajan, P. Thulasi, S. Priya, Role of association rules in medical examination records of Gestational Diabetes Mellitus, in 2017 International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India (2017), pp. 78–81

    Google Scholar 

  10. I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, I. Chouvarda, Machine learning and data mining methods in diabetes research. Comput. Struct. Biotechnol. J. 15, 104–116 (2017)

    Google Scholar 

  11. W. Altaf, M. Shahbaz, A. Guergachi, Applications of association rule mining in health informatics: a survey. Artif. Intell. Rev. 47(3), 313–340 (2016). https://doi.org/10.1007/s10462-016-9483-9. Accessed 17 Feb 2019

  12. H. Emblem, When to use a trimmed mean. Medium (2018). [Online]. https://medium.com/@HollyEmblem/when-to-use-a-trimmed-mean-fd6aab347e46. Accessed 05 Mar 2019

  13. Median Function R Documentation (2017). [Online]. https://www.rdocumentation.org/packages/stats/versions/3.5.2/topics/median. Accessed 10 Mar 2019

  14. A. Yosola, Association rule mining - apriori algorithm. NoteWorthy-The Journal Blog (2018). [Online]. https://blog.usejournal.com/association-rule-mining-apriori-algorithm-c517f8d7c54c. Accessed 12 Mar 2019

  15. A. Shah, Association rule mining with modified apriori algorithm using top down approach, in 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Bangalore, India (2016), pp. 747–752

    Google Scholar 

  16. U. Malik, Association rule mining via apriori algorithm in Python. Stack Abuse (2018). [Online]. https://stackabuse.com/association-rule-mining-via-apriori-algorithm-in-python/. Accessed 16 Mar 2019

  17. A. Bhatia, Yu-Wei, D. Chiu, Machine Learning with R Cookbook - Second Edition: Analyze Data and Build Predictive Models, 2nd edn. (Packt Publishing Ltd., Birmingham, 2017)

    Google Scholar 

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Correspondence to M. Raihan .

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Tanvir Islam, M., Raihan, M., Farzana, F., Ghosh, P., Ahmed Shaj, S. (2021). An Empirical Study on Diabetes Mellitus Prediction Using Apriori Algorithm. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-15-5148-2_48

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