Comparative Study of Various Data Mining Techniques for Early Prediction of Diabetes Disease

Authors

  • Santosh P. Shrikhande  School of Technology, S.R.T.M. University, Sub-Campus, Latur, Maharashtra, India
  • Prashant P. Agnihotri  School of Technology, S.R.T.M. University, Sub-Campus, Latur, Maharashtra, India

DOI:

https://doi.org//10.32628/CSEIT228139

Keywords:

Diabetes Mellitus, Diabetes Prediction, Data Mining Techniques, Machine Learning Classification Techniques

Abstract

Diabetes is one of the prevalent diseases in the word with a high mortality rate. This disease has created several health problems and side effects on other organs of the human body. Therefore, diagnosis of this disease at early stage is essential that can reduce the fatal rate of humans. There are several ways to diagnose the diabetes but early diagnosis is quite challenging task for the medical practitioners. Recently, data mining based techniques are widely used for early prediction of diabetes that gives promising results in diabetes prediction. This paper presents the detailed review of existing data mining techniques used for diabetes prediction with their comparative study. This study also provides analysis of existing methodologies that will help in future perspective for designing and developing novel diabetes predictive models.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-02-28

Issue

Volume 8, Issue 1, January-February-2022

Section

Research Articles

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How to Cite

[1]
Santosh P. Shrikhande, Prashant P. Agnihotri, " Comparative Study of Various Data Mining Techniques for Early Prediction of Diabetes Disease, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 1, pp.287-295, January-February-2022. Available at doi : https://doi.org/10.32628/CSEIT228139