Predictive Analysis of Diabetes Using Machine Learning Algorithms

Author:

Mansoor Nijatullah1,Chandra Poonia Ramesh1,Samanta Debabrata1ORCID

Affiliation:

1. CHRIST University (Deemed), India

Abstract

Diabetes is a very harmful disease that causes high blood sugar levels and occurs when the blood glucose level is high. Diabetes causes numerous diseases in humans: congestive heart failure, stroke, kidney and eye problems, dental issues, nerve damage, and foot problems. With the recent development in the machine learning concept, it is easy to analyze and predict whether a person is diabetic or not. This research mainly focuses on using several prediction algorithms of machine learning. The algorithms used in this research are k-nearest neighbor, logistic regression, SVM (support vector machine), Gaussian naive Bayes, decision tree, multilayer perceptron, random forest, XGBoost, and AdaBoost. Among these algorithms, the XGBoost performed better than the other algorithms achieving an accuracy of 90%, and the f1 score and Jaccard score were 91% and 86%, respectively. The primary goal of this research is to apply numerous machine learning algorithms to diabetic datasets, analyze their results, and select the best one that performs well.

Publisher

IGI Global

Reference24 articles.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3