Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This study aimed to investigate the feasibility of the directed cyclic graph (DCG) in the feature selection and modeling of dampness syndrome to objectively diagnose chronic gastritis (CG). The diagnostic information of patients with dampness syndrome was selected from 919 cases collected in our previous study. Relevant characteristic variables re chosen using the combined rough set and mutual information (RS-MI) method. These selected variables re then used to construct a DCG model. The selected variables re consistent with the symptoms described in traditional Chinese medicine (TCM). The classification accuracies of both dampness syndromes re determined through DCG modeling. The accuracies of the dampness-heat accumulating in the spleen-stomach and the dampness obstructing the spleen-stomach re 90.4% and 78.7%, respectively. Therefore, the DCG model was superior to Navie Bayes(NB) model in terms of classification ability. The classification accuracy rate of the DCG model of the dampness obstructing the spleen-stomach was higher by 1.1% than that of the NB model. In conclusion,feature selection and model construction methods can be used to objectively evaluate the TCM syndromes of CG; nevertheless, these methods should be further investigated and promoted.
['Wei-fei Xu', 'Guo-Ping Liu', 'Jianjun Yan', 'Yiqin Wang', 'Xiong Lu', 'Tao Zhong']
I am writing a research paper on a idea Directed cyclic graph-based feature selection and modeling of the dampness syndrome of chronic gastritis
[{"from": "human", "value": "I am writing a research paper on a idea Directed cyclic graph-based feature selection and modeling of the dampness syndrome of chronic gastritis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This study aimed to investigate the feasibility of the directed cyclic graph (DCG) in the feature selection and modeling of dampness syndrome to objectively diagnose chronic gastritis (CG). The diagnostic information of patients with dampness syndrome was selected from 919 cases collected in our previous study. Relevant characteristic variables re chosen using the combined rough set and mutual information (RS-MI) method. These selected variables re then used to construct a DCG model. The selected variables re consistent with the symptoms described in traditional Chinese medicine (TCM). The classification accuracies of both dampness syndromes re determined through DCG modeling. The accuracies of the dampness-heat accumulating in the spleen-stomach and the dampness obstructing the spleen-stomach re 90.4% and 78.7%, respectively. Therefore, the DCG model was superior to Navie Bayes(NB) model in terms of classification ability. The classification accuracy rate of the DCG model of the dampness obstructing the spleen-stomach was higher by 1.1% than that of the NB model. In conclusion,feature selection and model construction methods can be used to objectively evaluate the TCM syndromes of CG; nevertheless, these methods should be further investigated and promoted."}]