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Published in:

Volume 10 Issue 1
January-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2301452


Registration ID:
507809

Page Number

e334-e344

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Title

Comparative Analysis of Machine Learning Methods for Early-Stage Diabetes Prediction

Authors

Abstract

Diabetes is a chronic disease with the potential to cause a worldwide health care crisis. According to International Diabetes Federation 382 million people are living with diabetes across the whole world. By 2035, this will be doubled as 592 million. Diabetes is a disease caused due to the increase level of blood glucose. This high blood glucose produces the symptoms of frequent urination, increased thirst, and increased hunger. Diabetes is a one of the leading causes of blindness, kidney failure, amputations, heart failure and stroke. When we eat, our body turns food into sugars, or glucose. At that point, our pancreas is supposed to release insulin. Insulin serves as a key to open our cells, to allow the glucose to enter and allow us to use the glucose for energy. But with diabetes, this system does not work. Type 1 and type 2 diabetes are the most common forms of the disease, but there are also other kinds, such as gestational diabetes, which occurs during pregnancy, as well as other forms. Machine learning is an emerging scientific field in data science dealing with the ways in which machines learn from experience. The objective of this research is to present comparative analysis of diabetes prediction using Logistic Regression and Random Forest Classifier methods. Result of Random Forest classifier outperform the Logistic Regression method in terms of accuracy 98.07 % and ROC is 0.98.

Key Words

Machine Learning, Diabetes, Logistic Regression, Random Forest Classifier, Accuracy, Confusion matrix.

Cite This Article

"Comparative Analysis of Machine Learning Methods for Early-Stage Diabetes Prediction ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 1, page no.e334-e344, January-2023, Available :http://www.jetir.org/papers/JETIR2301452.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Comparative Analysis of Machine Learning Methods for Early-Stage Diabetes Prediction ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 1, page no. ppe334-e344, January-2023, Available at : http://www.jetir.org/papers/JETIR2301452.pdf

Publication Details

Published Paper ID: JETIR2301452
Registration ID: 507809
Published In: Volume 10 | Issue 1 | Year January-2023
DOI (Digital Object Identifier):
Page No: e334-e344
Country: Durg, Chhattisgarh, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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