Detection of Diabetes Using Various Machine Learning Algorithm

( An International Open Access , Peer-reviewed, Refereed Journal )

IJIRTEXPLORE- Search Thousands of research papers

ISSN: 2349-6002 ESTD Year: 2014

UGC approved journal no 47859

Detection of Diabetes Using Various Machine Learning Algorithm
Author(s):
Rishav Karanjit
Keywords:
Diabetes, Machine learning, Decision Tree, K-Nearest Neighbour, Random forest, soft voting classifier.
Abstract
Lot of the people around the globe are suffering from diabetics. Diabetes is caused by very high blood glucose. There are different tests available to test diabetes. All of these tests directly or indirectly require assistance from medical personnel. Machine learning can help individuals to detect diabetes without the need of medical personnel. In this paper, we have proposed the solution of detecting diabetes using Decision Tree, K-Nearest Neighbour, Random forest and soft voting classifier which achieved the accuracy of 80.52%, 75.97%, 81.17%, 83.12% respectively
Article Details
Unique Paper ID: 152260 Publication Volume & Issue: Volume 8, Issue 2 Page(s): 700 - 703
Article Preview & Download

Share This Article

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2021

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2021

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org