Detection of Diabetes Using Various Machine Learning Algorithm

Rishav Karanjit

Detection of Diabetes Using Various Machine Learning Algorithm

Authors: Rishav Karanjit

Unique Paper ID: 152260
Volume: 8
Issue: 2
PageNo: 700-703

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

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ISSN: 2349-6002
Volume: 8
Issue: 2
PageNo: 700-703

Detection of Diabetes Using Various Machine Learning Algorithm

Available:https://ijirt.org/Article?manuscript=152260

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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