Improving Children Diagnostics by Efficient Multi-label Classification Method
| Autor: | Danuta Zakrzewska, Kinga Glinka, Agnieszka Wosiak |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Multi-label classification Process (engineering) Computer science business.industry Binary number Patient data Medical decision making Machine learning computer.software_genre 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Relevance (information retrieval) Artificial intelligence business Hamming code computer 030215 immunology |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783319397955 ITIB (1) |
| DOI: | 10.1007/978-3-319-39796-2_21 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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