期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2018
卷号：6
期号：4
页码：3756-3763
DOI：10.15680/IJIRCCE.2018.0604088
出版社：S&S Publications
摘要：Machine learning algorithms will facilitate us to sight the onset endocrinology or diabetes disorder.
Early detection of endocrinology disorder will cut back patient’s health risk. Physicians, patients, and patient’s relatives
may be benefited from the prediction’s outcomes. In low resource clinical settings, it's necessary to predict the patient’s
condition once the onus to portion resources suitably measured and preventions can be demonstrated or exercised.
Many articles are revealed analyzing Prima Indian information set applying on numerous machine learning algorithms.
However, under this scheme using Linear Regression and LS-SVM Classification techniques to predict the onset of
diabetes on Prima Indian polygenic disorder dataset are demonstrated under this approach for such classification the
confusion matrix and variance from Least Square Support Vector Machine is reliable approach and can forecast the
unforeseen measures and symptoms foe endocrinology disorder. These techniques increase diagnosing accuracy and
cut back medical bills and ensure the health living. During this study, the most focus is to analyze differing types of
machine learning classification algorithms and show their amalgamated analysis. The aim of this study is to sight the
diabetic patient’s onset from the outcomes generated by machine learning classification algorithms.
关键词：Diabetes mellitus; Linear Regression; Decision Tree; Least Square Support Vector Machine;
