Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label / 中国结合医学杂志
Chinese journal of integrative medicine
;
(12): 867-871, 2016.
Artigo
em Inglês
| WPRIM
| ID: wpr-301015
ABSTRACT
<p><b>OBJECTIVE</b>To develop an effective Chinese Medicine (CM) diagnostic model of coronary heart disease (CHD) and to confifirm the scientifific validity of CM theoretical basis from an algorithmic viewpoint.</p><p><b>METHODS</b>Four types of objective diagnostic data were collected from 835 CHD patients by using a self-developed CM inquiry scale for the diagnosis of heart problems, a tongue diagnosis instrument, a ZBOX-I pulse digital collection instrument, and the sound of an attending acquisition system. These diagnostic data was analyzed and a CM diagnostic model was established using a multi-label learning algorithm (REAL).</p><p><b>RESULTS</b>REAL was employed to establish a Xin (Heart) qi defificiency, Xin yang defificiency, Xin yin defificiency, blood stasis, and phlegm fifive-card CM diagnostic model, which had recognition rates of 80.32%, 89.77%, 84.93%, 85.37%, and 69.90%, respectively.</p><p><b>CONCLUSIONS</b>The multi-label learning method established using four diagnostic models based on mutual information feature selection yielded good recognition results. The characteristic model parameters were selected by maximizing the mutual information for each card type. The four diagnostic methods used to obtain information in CM, i.e., observation, auscultation and olfaction, inquiry, and pulse diagnosis, can be characterized by these parameters, which is consistent with CM theory.</p>
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Síndrome
/
Algoritmos
/
Doença das Coronárias
/
Diagnóstico
/
Máquina de Vetores de Suporte
/
Medicina Tradicional Chinesa
Tipo de estudo:
Estudo diagnóstico
/
Estudo prognóstico
Limite:
Idoso
/
Humanos
Idioma:
Inglês
Revista:
Chinese journal of integrative medicine
Ano de publicação:
2016
Tipo de documento:
Artigo
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