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

Shivani Patel, Sanjay Chaudhary and Prakashsingh Tanwar

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019, vol. 5, issue 1, 612-620

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.

Keywords: Theoretical; Evaluation; dataset; Pre-Process; and machine learning (search for similar items in EconPapers)
Date: 2019
Note: Article URL: https://ijsrcseit.com/CSEIT217618
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/CSEIT217618 Article URL (text/html)
https://ijsrcseit.com/paper/CSEIT217618.pdf Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v5:y2019:i1:id:hcseit217618

DOI: 10.32628/CSEIT217618

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().