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IJSDR
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

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Volume 9 | Issue 3

Impact factor: 8.15

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Paper Title: Comparison of Various Machine Learning Algorithms for Diabetes Disease Prediction
Authors Name: Ashwni Kumar , Mariya Khatoon
Unique Id: IJSDR2006051
Published In: Volume 5 Issue 6, June-2020
Abstract: More than 31 million people in India suffer from diabetes and many people are at risk. People with diabetes are at increased risk of heart disease, stroke, eye problems and liver damage. Current hospital practice collects the information needed to diagnose diabetes through various tests and provides appropriate treatment based on the diagnosis. Big Data Analytics plays an important role in the healthcare industries. There is a comprehensive database of health care industries. Using big data analysis, you could study huge data sets and discover hidden information, hidden schemes to discover knowledge of data, and predict results accordingly. In this research paper, we have introduced the diabetes prediction model for better classification of diabetes, in which the patient is told with greater accuracy. Therefore, five machine learning classifications are used in this experiment, to identify diabetes, namely Naïve Bayesian, Decision Tree, Random Forest, Simple CART, and Support Vector Machine. The experiment is performed using the Pima Indian diabetes database that is sourced from the UCI machine learning repository on the WEKA tool. The performance of the five algorithms is evaluated on various measures such as Accuracy, Precision, Recall and F-measure. The obtained result shows a far better performance of the Support Vector Machine with the maximum accuracy of 79.87% compared to the other algorithms.
Keywords: Diabetes Disease Prediction, Machine Learning, Support Vector Machine, Decision Tree, Random Forest, Naïve Bayesian, Simple CART
Cite Article: "Comparison of Various Machine Learning Algorithms for Diabetes Disease Prediction ", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.5, Issue 6, page no.309 - 314, June-2020, Available :http://www.ijsdr.org/papers/IJSDR2006051.pdf
Downloads: 000336257
Publication Details: Published Paper ID: IJSDR2006051
Registration ID:191942
Published In: Volume 5 Issue 6, June-2020
DOI (Digital Object Identifier):
Page No: 309 - 314
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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