Abstract
This paper focuses on the attempt to formulate the prescription prediction logic based on the medical data analysis towards the future computer-assisted-diagnosis for Kampo medicine. We constructed and evaluated prediction models for some frequently-used prescriptions using six kinds of machine learning algorithms including artificial neural network, multinomial logit, random forest, support vector machine, k-nearest neighbor, and decision tree. The possibility of prescription prediction and the necessary amount of data required for robust prediction are clarified.
Original language | English |
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Title of host publication | Proceedings - 2015 International Conference on Computer Application Technologies, CCATS 2015 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 126-131 |
Number of pages | 6 |
ISBN (Electronic) | 9781467382113 |
DOIs | |
Publication status | Published - 2016 Jan 4 |
Event | 1st International Conference on Computer Application Technologies, CCATS 2015 - Matsue, Japan Duration: 2015 Aug 31 → 2015 Sep 2 |
Other
Other | 1st International Conference on Computer Application Technologies, CCATS 2015 |
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Country | Japan |
City | Matsue |
Period | 15/8/31 → 15/9/2 |
Keywords
- evid
- holistic
- induction
- Kampo medicine
- knowledge discovery
- machinery learning
- medical data
- objectification
- prescription prediction
- prescription-syndrome correspondence
- robust prediction
- statistical analysis
- tacit knowledge
- traditional medicine
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications