本文選題：類風濕關節(jié)炎 + 判別模型　； 參考：《北京中醫(yī)藥大學》2016年碩士論文

【摘要】：類風濕關節(jié)炎是一種以侵襲性關節(jié)炎為主要表現的全身性自身免疫病,中醫(yī)是在整體觀念指導下辨證論治,尤其在治未病以及對并發(fā)癥的治療上,適當的規(guī)避了西醫(yī)治療中無法早期診斷早期治療以及對并發(fā)癥的忽視治療等狀況。而且,中藥同西藥相比,毒副作用較小,且不良反應較少,既可扶正固本、調節(jié)機體免疫功能,又可改善微循環(huán)、抗炎、鎮(zhèn)痛,達到標本兼治的作用,更適合患者長期服用�！白C候”是辨證論治的核心,發(fā)現證候理論中所蘊含的客觀規(guī)律,構建辨證論治的規(guī)范依據,是中醫(yī)證候學研究的方向。證候研究的難點在于：首先,中醫(yī)臨證辨證方法多樣,證型不規(guī)范,難以進行證候標準化。第二,中醫(yī)證候是一個非線性的復雜系統(tǒng),多維多階,無限組合,單純運用還原的方法無法對其進行合理的闡釋。第三,臨床醫(yī)生對證候的判定過程信息復雜且高度融合,具有模糊性的特點。第四,各癥狀對證候診斷的鑒別意義不等,中醫(yī)證候難以量化和客觀化。研究目的：中醫(yī)臨證中的證候診斷過程,是醫(yī)生提取四診信息中對證候鑒別有意義的癥狀,并將這些癥狀進行分類的過程,證候問題實質上就是中醫(yī)癥狀的分類問題。數據挖掘領域中專門用于解決分類問題的方法被稱為分類算法。本研究將隨機森林算法引入到中醫(yī)證候的研究中來,試解決癥狀的重要性計算和證型分類問題。研究方法：針對證候研究中證候信息非線性,高維高階,模糊性,難以衡量各因素重要程度等問題,將數據挖掘領域中的分類算法引入到中醫(yī)證候診斷的研究過程中來,運用隨機森林對類風濕關節(jié)炎進行特征選擇,并構建證候分類模型；為驗證隨機森林模型效能,采用支持向量機方法進行建模作為對比實驗,對比兩模型預測準確率。結果：1.本研究以類風濕關節(jié)炎為研究對象,搜集RA文獻報道的中醫(yī)有效辨證信息,參考現有中醫(yī)證候分類標準,人工對辨證信息進行分型歸類,并對各證型下屬癥狀進行術語規(guī)范化處理,建立了一個“RA證-癥”數據集。2.采用隨機森林方法實現了對類風濕關節(jié)炎證型判別模型的構建,并對特征癥狀進行權重計算。3.采用支持向量機方法建立證型判別模型,兩種模型準確率對比結果顯示隨機森林性能優(yōu)異。結論：1.隨機森林模型在中醫(yī)證候建模過程中表現出了良好的性能,不僅準確率高,還能衡量癥狀在證候分類中的貢獻程度,找出對類風濕關節(jié)炎證候分類最有影響的主要癥狀,研究結果同現行的證候特點相對比,有助于證候表述的完善,適合引入應用于證候規(guī)范化研究。2.本研究采用中醫(yī)證候研究中應用較為成熟廣泛的支持向量機方法對同一數據集進行分類建模作為對比實驗,研究結果顯示隨機森林模型預測準確率同支持向量機具有可比性,而且模型性能更為穩(wěn)定,這在一定程度上可以證明將隨機森林方法引入用于證候研究具有可觀前景。3.隨機森林方法的一大顯著優(yōu)點是在建模過程中能對特征的重要性進行計算,體現在本研究中是實現了對類風濕關節(jié)炎證型分類的特征癥狀的重要性排序,篩選出了對證型判別最有意義的癥狀特征,這有助于更好的解釋模型豐富證候的特異性表述,也為解決證候數據的冗余性提供了一種新的方法,而且為證候研究中的難點定量研究提供了一種新的可能性。
[Abstract]:Rheumatoid arthritis is a systemic autoimmune disease characterized by invasive arthritis. Traditional Chinese medicine is treated with a syndrome differentiation under the guidance of the whole concept, especially in the treatment of the disease and the treatment of complications. It is appropriate to avoid the early diagnosis of early treatment in the treatment of Western medicine and the neglect of the complications. Traditional Chinese medicine, compared with western medicine, has smaller side effects and less adverse reactions. It can not only help to fix the solid, regulate the immune function of the body, but also improve the microcirculation, anti-inflammatory and analgesic effect, which is more suitable for the long-term use of the patient. "Syndrome" is the core of the syndrome differentiation and treatment, and finds the objective law contained in the syndrome theory and constructs the syndrome differentiation theory. The standard basis of treatment is the direction of TCM syndrome research. The difficulty of syndrome research lies in: first, the syndrome differentiation method of TCM syndrome is diverse, the syndrome type is not standardized and the syndrome is difficult to standardize. Second, TCM syndrome is a nonlinear complex system, multidimensional and multi order, unlimited combination, and the simple method of using reduction can not be reasonable. Third, the diagnosis process of the syndromes of the clinicians is complex and highly fused and has the characteristics of fuzziness. Fourth, the differential significance of the symptoms to the syndrome diagnosis is different, the TCM syndromes are difficult to quantify and objectified. The symptoms are the classification of these symptoms, and the syndrome is essentially the classification of TCM symptoms. The methods used to solve the classification problems in the field of data mining are called classification algorithms. This study introduces the random forest algorithm into the research of TCM syndrome, and tries to solve the importance calculation and syndrome classification of the symptoms. Research methods: in view of the problems of syndrome information nonlinear, high elevation and fuzziness, it is difficult to measure the importance of various factors in the study of syndrome, and the classification algorithms in the field of data mining are introduced into the research process of TCM syndrome diagnosis, and the characteristics of rheumatoid arthritis are selected and the syndromes are constructed by using random forest. In order to verify the effectiveness of the random forest model, the support vector machine is used to model the model as a contrast experiment, and the accuracy of the two model is compared. The results are as follows: 1. this study takes rheumatoid arthritis as the research object, collecting the effective syndrome differentiation information reported in RA literature, referring to the existing TCM syndrome classification standards, and artificial information on the syndrome differentiation information. The classification and classification were carried out, and the symptoms of each type of syndrome were normalized, and a "RA syndrome" data set was established..2. was constructed by the random forest method, and the weight calculation of the characteristic symptoms.3. was established by the support vector machine, and the two models were established. The results showed that the performance of the random forest was excellent. Conclusion: the 1. random forest model showed good performance in the process of TCM syndrome modeling. It not only had high accuracy, but also measured the contribution degree of symptoms in the classification of syndromes, found the most influential symptoms of the syndrome classification of rheumatoid arthritis, and the results were the same. The comparison of the characteristics of the syndromes is helpful to the perfection of the expression of syndromes. It is suitable for the introduction and application of the research on the standardization of syndrome..2. this study uses a more mature and extensive support vector machine method to classify the same data set in the study of TCM syndrome as a contrast experiment. The results show that the accuracy of the prediction of the random forest model is the same. The support vector machine has the comparability and the model performance is more stable. This can prove to some extent that a significant advantage of introducing the random forest method into the observable.3. random forest method is that the importance of the characteristics can be calculated in the modeling process, which is embodied in the study. The importance of characteristic symptoms in the syndrome classification of rheumatoid arthritis is sorted, and the most significant symptom features are screened out. This helps to explain the specific expression of the model rich syndrome better, and provides a new method for solving the redundancy of syndrome data, and provides a quantitative study of the difficulties in the study. A new possibility.
【學位授予單位】：北京中醫(yī)藥大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：R259

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