Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis
المؤلفون المشاركون
Wang, Yi-Qin
Yan, Jian-Jun
Liu, Guo-Ping
Fu, Jing-Jing
Qian, Peng
Guo, Rui
Xu, Zhao-Xia
المصدر
Evidence-Based Complementary and Alternative Medicine
العدد
المجلد 2012، العدد 2012 (31 ديسمبر/كانون الأول 2012)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2012-06-03
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Background.
In Traditional Chinese Medicine (TCM), most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning.
However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs).
Methods.
We employed a multilabel learning using the relevant feature for each label (REAL) algorithm to construct a syndrome diagnostic model for chronic gastritis (CG) in TCM.
REAL combines feature selection methods to select the significant symptoms (signs) of CG.
The method was tested on 919 patients using the standard scale.
Results.
The highest prediction accuracy was achieved when 20 features were selected.
The features selected with the information gain were more consistent with the TCM theory.
The lowest average accuracy was 54% using multi-label neural networks (BP-MLL), whereas the highest was 82% using REAL for constructing the diagnostic model.
For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively.
Conclusion.
REAL extracts the relevant symptoms (signs) for each syndrome and improves its recognition accuracy.
Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Liu, Guo-Ping& Yan, Jian-Jun& Wang, Yi-Qin& Fu, Jing-Jing& Xu, Zhao-Xia& Guo, Rui…[et al.]. 2012. Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis. Evidence-Based Complementary and Alternative Medicine،Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-1027998
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Liu, Guo-Ping…[et al.]. Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis. Evidence-Based Complementary and Alternative Medicine No. 2012 (2012), pp.1-9.
https://search.emarefa.net/detail/BIM-1027998
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Liu, Guo-Ping& Yan, Jian-Jun& Wang, Yi-Qin& Fu, Jing-Jing& Xu, Zhao-Xia& Guo, Rui…[et al.]. Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis. Evidence-Based Complementary and Alternative Medicine. 2012. Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-1027998
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1027998
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر