Thyroid Disease Prediction Using Machine Learning Approaches

被引：29

作者：

Chaubey, Gyanendra ^{[1
]}

Bisen, Dhananjay ^{[1
]}

Arjaria, Siddharth ^{[1
]}

Yadav, Vibhash ^{[1
]}

机构：

[1] Rajkiya Engn Coll, Dept Informat Technol, Atarra 210201, Banda, India

来源：

NATIONAL ACADEMY SCIENCE LETTERS-INDIA | 2021年 / 44卷 / 03期

关键词：

Machine learning; Decision tree; Thyroid disease; k-nearest neighbor; Logistic regression;

D O I：

10.1007/s40009-020-00979-z

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

This paper is being written to provide a source of reference for the research scholars who want to work in the area of prediction of thyroid disease. From the different machine learning techniques, compared widely used three algorithms namely logistic regression, decision trees and k-nearest neighbor (kNN) algorithms to predict and evaluate their performance in terms of accuracy. This study has represented the intuition of how to predict the thyroid disease and highlighted how to apply the logistic regression, decision trees and kNN as a tool for the classification. For this, thyroid data set of machine learning repository has used from UC Irvin knowledge discovery in databases archive.

引用

页码：233 / 238

页数：6

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