Improving Children Diagnostics by Efficient Multi-label Classification Method

被引：12

作者：

Glinka, Kinga ^{[1
]}

Wosiak, Agnieszka ^{[1
]}

Zakrzewska, Danuta ^{[1
]}

机构：

[1] Lodz Univ Technol, Inst Informat Technol, Wolczanska 215, Lodz, Poland

来源：

INFORMATION TECHNOLOGIES IN MEDICINE, ITIB 2016, VOL 1 | 2016年 / 471卷

关键词：

Children diagnostics; Problem transformation methods; Labels chain; Multi-label classification; HYPERTENSION;

D O I：

10.1007/978-3-319-39796-2_21

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Using intelligent computational methods may support children diagnostics process. As in many cases patients are affected by multiple illnesses, multi-perspective view on patient data is necessary to improve medical decision making. In the paper, multi-label classification method-Labels Chain is considered. It performs well when the number of attributes significantly exceeds the number of instances. The effectiveness of the method is checked by experiments conducted on real data. The obtained results are evaluated by using two metrics: Classification Accuracy and Hamming Loss, and compared to the effects of the most popular techniques: Binary Relevance and Label Power-set.

引用

页码：253 / 266

页数：14

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