A Theoretical Evaluation of Mellitus Diabetes using Data Mining and Machine Learning

Authors

  • Shivani Patel  Research Scholar, Computer Science And Information Technology, Madhav University, Pindwara (Sirohi) Rajasthan, India
  • Sanjay Chaudhary  Research Supervisor, Computer Science And Information Technology, Madhav University, Pindwara (Sirohi) Rajasthan, India
  • Prakashsingh Tanwar  Computer Science And Information Technology, Madhav University, Madhav University, Pindwara (Sirohi) Rajasthan, India

DOI:

https://doi.org//10.32628/CSEIT217618

Keywords:

Theoretical, Evaluation, dataset, Pre-Process, and machine learning

Abstract

Pattern identification, processing, and treatment are all common uses of data mining techniques in medical diagnostics. Diabetes is a metabolic illness in which elevated blood sugar levels persist for an extended period of time. Diabetes mellitus (DM) is a collection of metabolic illnesses that puts a lot of pressure on people all over the world. According to these studies, India accounts for 19% of the world's residents. Category 1 and Category 2 diabetes are covered in this overview. Theoretical basis is used to compare previous researcher methodologies and processes. To process datasets, the Weka open-source tool is employed. In the first half, we'll talk about gathering data from various medical departments; in the second part, we'll talk about data cleaning and then algorithms for removing noisy data. Also, several Algorithms were used to determine the best characteristic. Finally, we'll look at alternative machine learners for diabetes data classification and discuss future research directions.

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

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Published

2019-02-27

Issue

Volume 5, Issue 1, January-February-2019

Section

Research Articles

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How to Cite

[1]
Shivani Patel, Sanjay Chaudhary, Prakashsingh Tanwar, " A Theoretical Evaluation of Mellitus Diabetes using Data Mining and Machine Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.612-620, January-February-2019. Available at doi : https://doi.org/10.32628/CSEIT217618