[Show abstract][Hide abstract] ABSTRACT: Background. We illustrated an example of structure equation modelling (SEM) in the research on SHS to explore the diagnosis of the Sub optimal health status (SHS) and provide evidence for the standardization of traditional Chinese medicine (TCM) patterns in SHS. And the diagnosis of 4 TCM patterns in SHS was evaluated in this analysis. Methods. This study assessed data on 2807 adults (aged 18 to 49) with SHS from 6 clinical centres. SEM was used to analyze the patterns of SHS in TCM. Parameters in the introduced model were estimated by the maximum likelihood method. Results. The discussed model fits the SHS data well with CFI = 0.851 and RMSEA = 0.075. The direct effect of Qi deficiency pattern on dampness pattern had the highest magnitude (value of estimate is 0.822). With regard to the construct of "Qi deficiency pattern", "fire pattern", "stagnation pattern" and "dampness pattern", the indicators with the highest load were myasthenia of limbs, vexation, deprementia, and dizziness, respectively. It had been shown that estimate factor should indicate the important degree of different symptoms in pattern. Conclusions. The weights of symptoms in the respective pattern can be statistical significant and theoretical meaningful for the 4 TCM patterns identification in SHS research. The study contributed to a theoretical framework, which has implications for the diagnosis points of SHS.
Evidence-based Complementary and Alternative Medicine 01/2012; 2012:970985. · 1.72 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Sub-health state is a low-quality status between health and disease. The aim of this study was to determine which factors and/or combination of factors could be predictive of sub-health state. In this paper, we carried out a clinical epidemiology survey and obtained two datasets both of which include 50 symptoms in report. The Dataset 1 consists of 572 samples, of which 523 cases were in sub-health state and 49 cases were in healthy. The Dataset 2 consists of 185 samples, of which 131 cases were in sub-health state and 54 cases were in healthy. The Dataset 1 was used to select variables and estimate the performance of the classifier built by SVM, while the Dataset 2 was used to validate the performance of the classifier based on the Dataset 1. Based on association declined by mutual information, we propose a feature selection method based on support vector machine recursive feature elimination (SVM-RFE) to predict the sub-health state from the analysis of the clinical data. We have considered optimal performance at the threshold where sensitivity and specificity were respectively 0.82 and 0.72. The performance of this method achieved an average prediction accuracy of 80.35%. The top 8 features (symptoms) selected by SVM-RFE were as follows: Fatigue, Degree of insomnia, Pessimism, Constipation, Dysphoria, Giddiness, Anorexia and Vexation. Therefore, we propose a new method for feature selection in classification problems that uses SVM-RFE. The goal is to remove too many features during each iteration, but not to eliminate the important one.
Artificial Intelligence and Computational Intelligence (AICI), 2010 International Conference on; 11/2010
[Show abstract][Hide abstract] ABSTRACT: BACKGROUND: Sub-health state is a low-quality status between health and disease. The aim of this study was to determine which factors and/or combination of factors could be predictive of sub-health state in female as using random forest method. METHODS: Data were collected through a clinical epidemiology survey and obtained 2992 cases (2507 cases were in sub-health state and 485 cases were in health), in which the female subhealth state cases were 1285 and the female health state cases were 177, respectively. Based on association declined by mutual information, we used a classification technique called Random Forest to predict the sub-health state in female through the analysis of the clinical data. RESULTS: We've obtained the total OOB error rate of 20.06% , namely, the correct classification rate is 79.94%. In other words, there were 10 variables very powerful to discriminate between health state and sub-health state in female. They were the symptoms as follows, Fatigue, Myasthenia of limbs, Amnesia, Dizziness, Dysphoria, Sighing, Hypochondriac distension and pain, Constipation, Swollen sore throat and Premenstrual Distension of Breast. CONCLUSIONS: We suggest data random forest mining method for feature selection in female sub-health state; the main advantage of this method is to select important features that retaining a high predictive accuracy.