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

【摘要】：類風(fēng)濕關(guān)節(jié)炎是一種以侵襲性關(guān)節(jié)炎為主要表現(xiàn)的全身性自身免疫病,中醫(yī)是在整體觀念指導(dǎo)下辨證論治,尤其在治未病以及對(duì)并發(fā)癥的治療上,適當(dāng)?shù)囊?guī)避了西醫(yī)治療中無法早期診斷早期治療以及對(duì)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(yīng)較少,既可扶正固本、調(diào)節(jié)機(jī)體免疫功能,又可改善微循環(huán)、抗炎、鎮(zhèn)痛,達(dá)到標(biāo)本兼治的作用,更適合患者長(zhǎng)期服用�！白C候”是辨證論治的核心,發(fā)現(xiàn)證候理論中所蘊(yùn)含的客觀規(guī)律,構(gòu)建辨證論治的規(guī)范依據(jù),是中醫(yī)證候?qū)W研究的方向。證候研究的難點(diǎn)在于：首先,中醫(yī)臨證辨證方法多樣,證型不規(guī)范,難以進(jìn)行證候標(biāo)準(zhǔn)化。第二,中醫(yī)證候是一個(gè)非線性的復(fù)雜系統(tǒng),多維多階,無限組合,單純運(yùn)用還原的方法無法對(duì)其進(jìn)行合理的闡釋。第三,臨床醫(yī)生對(duì)證候的判定過程信息復(fù)雜且高度融合,具有模糊性的特點(diǎn)。第四,各癥狀對(duì)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(duì)證候鑒別有意義的癥狀,并將這些癥狀進(jìn)行分類的過程,證候問題實(shí)質(zhì)上就是中醫(yī)癥狀的分類問題。數(shù)據(jù)挖掘領(lǐng)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(jī)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(jì)算和證型分類問題。研究方法：針對(duì)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(shù)據(jù)挖掘領(lǐng)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(yùn)用隨機(jī)森林對(duì)類風(fēng)濕關(guān)節(jié)炎進(jìn)行特征選擇,并構(gòu)建證候分類模型；為驗(yàn)證隨機(jī)森林模型效能,采用支持向量機(jī)方法進(jìn)行建模作為對(duì)比實(shí)驗(yàn),對(duì)比兩模型預(yù)測(cè)準(zhǔn)確率。結(jié)果：1.本研究以類風(fēng)濕關(guān)節(jié)炎為研究對(duì)象,搜集RA文獻(xiàn)報(bào)道的中醫(yī)有效辨證信息,參考現(xiàn)有中醫(yī)證候分類標(biāo)準(zhǔn),人工對(duì)辨證信息進(jìn)行分型歸類,并對(duì)各證型下屬癥狀進(jìn)行術(shù)語規(guī)范化處理,建立了一個(gè)“RA證-癥”數(shù)據(jù)集。2.采用隨機(jī)森林方法實(shí)現(xiàn)了對(duì)類風(fēng)濕關(guān)節(jié)炎證型判別模型的構(gòu)建,并對(duì)特征癥狀進(jìn)行權(quán)重計(jì)算。3.采用支持向量機(jī)方法建立證型判別模型,兩種模型準(zhǔn)確率對(duì)比結(jié)果顯示隨機(jī)森林性能優(yōu)異。結(jié)論：1.隨機(jī)森林模型在中醫(yī)證候建模過程中表現(xiàn)出了良好的性能,不僅準(zhǔn)確率高,還能衡量癥狀在證候分類中的貢獻(xiàn)程度,找出對(duì)類風(fēng)濕關(guān)節(jié)炎證候分類最有影響的主要癥狀,研究結(jié)果同現(xiàn)行的證候特點(diǎn)相對(duì)比,有助于證候表述的完善,適合引入應(yīng)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(yīng)用較為成熟廣泛的支持向量機(jī)方法對(duì)同一數(shù)據(jù)集進(jìn)行分類建模作為對(duì)比實(shí)驗(yàn),研究結(jié)果顯示隨機(jī)森林模型預(yù)測(cè)準(zhǔn)確率同支持向量機(jī)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(jī)森林方法引入用于證候研究具有可觀前景。3.隨機(jī)森林方法的一大顯著優(yōu)點(diǎn)是在建模過程中能對(duì)特征的重要性進(jìn)行計(jì)算,體現(xiàn)在本研究中是實(shí)現(xiàn)了對(duì)類風(fēng)濕關(guān)節(jié)炎證型分類的特征癥狀的重要性排序,篩選出了對(duì)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(shù)據(jù)的冗余性提供了一種新的方法,而且為證候研究中的難點(diǎn)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(xué)位授予單位】：北京中醫(yī)藥大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：R259

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 錢維;王超;吳騁;許金芳;葉小飛;杜文民;賀佳;;運(yùn)用隨機(jī)森林分析藥品不良反應(yīng)發(fā)生的影響因素[J];中國(guó)衛(wèi)生統(tǒng)計(jì);2013年02期

2 武曉巖;李康;;隨機(jī)森林方法在基因表達(dá)數(shù)據(jù)分析中的應(yīng)用及研究進(jìn)展[J];中國(guó)衛(wèi)生統(tǒng)計(jì);2009年04期

3 李貞子;張濤;武曉巖;李康;;隨機(jī)森林回歸分析及在代謝調(diào)控關(guān)系研究中的應(yīng)用[J];中國(guó)衛(wèi)生統(tǒng)計(jì);2012年02期

4 劉永春;宋弘;;基于隨機(jī)森林的乳腺腫瘤診斷研究[J];電視技術(shù);2014年15期

5 聶斌;王卓;杜建強(qiáng);朱明峰;林劍鳴;艾國(guó)平;熊玲珠;;基于粗糙集和隨機(jī)森林算法輔助糖尿病并發(fā)癥分類研究[J];江西師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2014年03期

6 武曉巖;李康;;基因表達(dá)數(shù)據(jù)判別分析的隨機(jī)森林方法[J];中國(guó)衛(wèi)生統(tǒng)計(jì);2006年06期

7 武曉巖;閆曉光;李康;;基因表達(dá)數(shù)據(jù)的隨機(jī)森林逐步判別分析方法[J];中國(guó)衛(wèi)生統(tǒng)計(jì);2007年02期

8 馬廣立;趙筱萍;程翼宇;;基于隨機(jī)森林與Chemistry Development Kit描述符的P-gp底物識(shí)別[J];高等學(xué)�；瘜W(xué)學(xué)報(bào);2007年10期

9 苑婕;李曉杰;陳超;宋向崗;王淑美;;基于隨機(jī)森林算法的川芎成分-靶點(diǎn)-疾病網(wǎng)絡(luò)的預(yù)測(cè)研究[J];中國(guó)中藥雜志;2014年12期

10 ;[J];;年期

相關(guān)會(huì)議論文 前7條

1 謝程利;王金橋;盧漢清;;核森林及其在目標(biāo)檢測(cè)中的應(yīng)用[A];第六屆和諧人機(jī)環(huán)境聯(lián)合學(xué)術(shù)會(huì)議（HHME2010)、第19屆全國(guó)多媒體學(xué)術(shù)會(huì)議（NCMT2010）、第6屆全國(guó)人機(jī)交互學(xué)術(shù)會(huì)議（CHCI2010）、第5屆全國(guó)普適計(jì)算學(xué)術(shù)會(huì)議（PCC2010）論文集[C];2010年

2 武曉巖;方慶偉;;基因表達(dá)數(shù)據(jù)分析的隨機(jī)森林方法及算法改進(jìn)[A];黑龍江省第十次統(tǒng)計(jì)科學(xué)討論會(huì)論文集[C];2008年

3 張?zhí)忑?梁龍;王康;李華;;隨機(jī)森林結(jié)合激光誘導(dǎo)擊穿光譜技術(shù)用于的鋼鐵分類[A];中國(guó)化學(xué)會(huì)第29屆學(xué)術(shù)年會(huì)摘要集——第19分會(huì)：化學(xué)信息學(xué)與化學(xué)計(jì)量學(xué)[C];2014年

4 相玉紅;張卓勇;;組蛋白去乙�；敢种苿┑臉�(gòu)效關(guān)系研究[A];第十一屆全國(guó)計(jì)算（機(jī)）化學(xué)學(xué)術(shù)會(huì)議論文摘要集[C];2011年

5 張濤;李貞子;武曉巖;李康;;隨機(jī)森林回歸分析方法及在代謝組學(xué)中的應(yīng)用[A];2011年中國(guó)衛(wèi)生統(tǒng)計(jì)學(xué)年會(huì)會(huì)議論文集[C];2011年

6 馮飛翔;馮輔周;江鵬程;劉菁;劉建敏;;隨機(jī)森林和k-近鄰法在某型坦克變速箱狀態(tài)識(shí)別中的應(yīng)用[A];第八屆全國(guó)轉(zhuǎn)子動(dòng)力學(xué)學(xué)術(shù)討論會(huì)論文集[C];2008年

7 曹東升;許青松;梁逸曾;陳憲;李洪東;;組合樹的集合體和后向消除策略去分類P-糖蛋白化合物[A];第十屆全國(guó)計(jì)算(機(jī))化學(xué)學(xué)術(shù)會(huì)議論文摘要集[C];2009年

相關(guān)博士學(xué)位論文 前4條

1 曹正鳳;隨機(jī)森林算法優(yōu)化研究[D];首都經(jīng)濟(jì)貿(mào)易大學(xué);2014年

2 雷震;隨機(jī)森林及其在遙感影像處理中應(yīng)用研究[D];上海交通大學(xué);2012年

3 岳明;基于隨機(jī)森林和規(guī)則集成法的酒類市場(chǎng)預(yù)測(cè)與發(fā)展戰(zhàn)略[D];天津大學(xué);2008年

4 李書艷;單點(diǎn)氨基酸多態(tài)性與疾病相關(guān)關(guān)系的預(yù)測(cè)及其機(jī)制研究[D];蘭州大學(xué);2010年

相關(guān)碩士學(xué)位論文 前10條

1 錢維;藥品不良反應(yīng)監(jiān)測(cè)中隨機(jī)森林方法的建立與實(shí)現(xiàn)[D];第二軍醫(yī)大學(xué);2012年

2 韓燕龍;基于隨機(jī)森林的指數(shù)化投資組合構(gòu)建研究[D];華南理工大學(xué);2015年

3 賀捷;隨機(jī)森林在文本分類中的應(yīng)用[D];華南理工大學(xué);2015年

4 張文婷;交通環(huán)境下基于改進(jìn)霍夫森林的目標(biāo)檢測(cè)與跟蹤[D];華南理工大學(xué);2015年

5 李強(qiáng);基于多視角特征融合與隨機(jī)森林的蛋白質(zhì)結(jié)晶預(yù)測(cè)[D];南京理工大學(xué);2015年

6 朱玟謙;一種收斂性隨機(jī)森林在人臉檢測(cè)中的應(yīng)用研究[D];武漢理工大學(xué);2015年

7 肖宇;基于序列圖像的手勢(shì)檢測(cè)與識(shí)別算法研究[D];電子科技大學(xué);2014年

8 李慧;一種改進(jìn)的隨機(jī)森林并行分類方法在運(yùn)營(yíng)商大數(shù)據(jù)的應(yīng)用[D];電子科技大學(xué);2015年

9 趙亞紅;面向多類標(biāo)分類的隨機(jī)森林算法研究[D];哈爾濱工業(yè)大學(xué);2014年

10 黎成;基于隨機(jī)森林和ReliefF的致病SNP識(shí)別方法[D];西安電子科技大學(xué);2014年

，

本文編號(hào)：2056889