A machine learning software tool for multiclass classification

被引：2

作者：

Wang, Shangzhou ^{[1
]}

Lu, Haohui ^{[1
]}

Khan, Arif ^{[1
]}

Hajati, Farshid ^{[2
]}

Khushi, Matloob ^{[3
,4
]}

Uddin, Shahadat ^{[1
]}

机构：

[1] Univ Sydney, Fac Engn, Sch Project Management, Level 2,21 Ross St, Forest Lodge, NSW 2037, Australia

[2] Victoria Univ Sydney, Coll Engn & Sci, 160 Sussex St, Sydney, NSW 2000, Australia

[3] Univ Suffolk, Ipswich, Suffolk, England

[4] Univ Sydney, Sch Comp Sci, Sydney, NSW, Australia

来源：

SOFTWARE IMPACTS | 2022年 / 13卷

关键词：

Disease comorbidity; Disease multimorbidity; Machine learning; Multiclass classification;

D O I：

10.1016/j.simpa.2022.100383

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

This paper describes code for a published article that can assist researchers with multiclass classification problems and analyse the performances of various machine learning models. Further, feature importance, feature correlation, variable clustering, confusion matrix and kernel density estimation were also implemented. The original study was published in Expert Systems with Applications, and this paper explains the code and workflow. Administrative healthcare data has been used as an example to run the code. The results and insights can assist healthcare stakeholders and policymakers reduce the negative impact of illness comorbidity and multimorbidity.

引用

页数：4

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