本文選題：血管性認(rèn)知障礙 + 決策樹��； 參考：《青島大學(xué)》2017年碩士論文

【摘要】：目的收集腦血管病患者社會人口學(xué)、生活模式和臨床疾病因素,分析導(dǎo)致血管性認(rèn)知障礙(VCI),非癡呆型血管性認(rèn)知障礙(VCIND),血管性癡呆(Va D)的影響因素,建立VCI、VCIND、Va D的影響因素模型。方法依據(jù)腦血管病診斷標(biāo)準(zhǔn),選取2014年10月至2016年10月于本院老年醫(yī)學(xué)科和神經(jīng)內(nèi)科住院治療的腦血管病患者505例,進(jìn)行社會人口學(xué)、生活模式和臨床疾病因素問卷調(diào)查和數(shù)據(jù)采集,隨機(jī)數(shù)字表法將患者分為訓(xùn)練集組(421例)與測試集組(84例),依據(jù)VCI等診斷標(biāo)準(zhǔn)將訓(xùn)練集患者分為非VCI組(225例)和VCI組(196例),VCI組又分為Va D組(98例)和VCIND組(98例),采用決策樹方法和Logistic回歸分析腦血管病患者發(fā)生VCI、VCIND、Va D的影響因素,建立發(fā)生VCI、VCIND、Va D的影響因素決策樹模型和Logistic回歸模型。結(jié)果1.通過訓(xùn)練集建立VCI決策樹模型,其交叉驗證模型識別準(zhǔn)確度為73.63%,對測試集的預(yù)測準(zhǔn)確度為73.81%,模型穩(wěn)定,擬合較好。飲酒、業(yè)余愛好、飲茶、受教育程度、高血壓、睡眠、年齡、飲食、糖尿病、體育鍛煉是發(fā)生VCI的10個分類節(jié)點變量影響因素,根節(jié)點為飲酒。Logistic回歸分析顯示,飲酒、受教育程度、體育鍛煉、糖尿病4個因素為腦血管病患者發(fā)生VCI的影響因素,該模型預(yù)測準(zhǔn)確度為66.98%,對測試集的預(yù)測準(zhǔn)確度為53.57%。決策樹模型的受試者工作特征曲線(AUC)為0.737(95%CI 0.688~0.786),Logistic回歸模型的AUC為0.664(95%CI 0.612~0.717)。2.通過訓(xùn)練集建立VCIND決策樹模型,其交叉驗證模型識別準(zhǔn)確度為81.73%,對測試集的預(yù)測準(zhǔn)確度為58.49%。受教育程度、高血脂、業(yè)余愛好、糖尿病、睡眠、飲食、體育鍛煉發(fā)生VCIND的7個分類點變量影響因素,根節(jié)點是受教育程度。Logistic回歸分析顯示,飲酒、受教育程度、體育鍛煉、糖尿病4個因素為腦血管病患者發(fā)生VCIND的影響因素,其預(yù)測準(zhǔn)確度為72.14%,用測試集數(shù)據(jù)檢驗?zāi)Ｐ?對測試集的預(yù)測準(zhǔn)確度為54.72%。VCIND決策樹模型的AUC為0.716(95%CI 0.648~0.784),Logistic回歸模型的AUC為0.596(95%CI 0.525~0.666)。3.通過訓(xùn)練集建立的VaD模型,交叉驗證模型識別準(zhǔn)確度為71.43%,對測試集的預(yù)測準(zhǔn)確度為69.42%,模型穩(wěn)定,擬合較好。飲茶、業(yè)余愛好、睡眠、年齡、糖尿病、飲酒等影響Va D發(fā)生分類點變量影響因素,其中根節(jié)點為飲茶。Logistic回歸分析結(jié)果顯示,業(yè)余愛好、飲茶、睡眠3個變量是發(fā)生Va D的影響因素,該模型預(yù)測準(zhǔn)確度為64.80%,對測試集的預(yù)測準(zhǔn)確度為53.92%。決策樹模型的AUC為0.714(95%CI 0.641~0.788),Logistic回歸模型的AUC為0.648(95%CI 0.571~0.725)。結(jié)論1.在對不同程度的血管性認(rèn)知障礙發(fā)生預(yù)測方面,決策樹模型優(yōu)于Logistic回歸模型。2.過量飲酒、糖尿病、高血壓、高脂飲食、失眠因素是腦血管病發(fā)生VCI的危險因素;有業(yè)余愛好、高教育水平、參加體育鍛煉、飲茶是其保護(hù)因素。3.高血脂、糖尿病、失眠、高脂飲食是VCIND發(fā)生的危險因素;有業(yè)余愛好、高教育水平、參加體育鍛煉是其的保護(hù)因素。4.失眠、高齡、有糖尿病、飲酒是影響腦血管病患者發(fā)生Va D的危險因素;飲茶、有業(yè)余愛好是其保護(hù)性因素。
[Abstract]:Objective to collect social demography, life pattern and clinical disease factors in patients with cerebrovascular disease, analyze the factors that lead to vascular cognitive impairment (VCI), non dementia vascular cognitive impairment (VCIND) and vascular dementia (Va D), and establish the influence factor model of VCI, VCIND, Va D. Methods according to the diagnostic criteria of cerebrovascular disease, select from October 2014 to 20 In October, 505 cases of cerebrovascular disease were hospitalized in the Department of geriatrics and neurology of our hospital. Social demography, life pattern and clinical disease factors were investigated and data collected. The patients were divided into training set (421 cases) and test set (84 cases) by random digital table, and the patients were divided into non - training sets according to VCI and other diagnostic criteria. In group VCI (225 cases) and group VCI (196 cases), group VCI was divided into Va D group (98 cases) and VCIND group (98 cases). The decision tree method and Logistic regression analysis were used to analyze the influencing factors of VCI, VCIND, Va D in patients with cerebrovascular disease, and the decision tree model and regression model were established. Results 1. set up the decision tree by training set. The accuracy of the cross validation model was 73.63%, the accuracy of the test set was 73.81%, the model was stable, and the fitting was good. Drinking, hobbies, drinking tea, education, hypertension, sleep, age, diet, diabetes, physical exercise were the 10 factors influencing the occurrence of VCI, and the root node was.Logistic regression. The analysis showed that drinking, education, physical exercise, and diabetes were 4 factors affecting the incidence of VCI in patients with cerebrovascular disease. The predictive accuracy of the model was 66.98%, and the predictive accuracy of the test set was 0.737 (95%CI 0.688~0.786) for the 53.57%. decision tree model (95%CI 0.688~0.786), and the AUC of the Logistic regression model was 0.66. 4 (95%CI 0.612~0.717).2. established the VCIND decision tree model through the training set. The accuracy of the cross validation model was 81.73%. The prediction accuracy of the test set was 58.49%. education, hyperlipidemia, hobbies, diabetes, sleep, diet, and physical exercise of the 7 classification point variables of VCIND. The root node was educated. .Logistic regression analysis showed that drinking, education, physical exercise, and diabetes were 4 factors affecting VCIND in patients with cerebrovascular disease, with a predictive accuracy of 72.14%, a test set data test model, and a AUC of 0.716 (95%CI 0.648~0.784) and Logistic regression for the prediction accuracy of the test set of the 54.72%.VCIND decision tree model (95%CI 0.648~0.784). The model's AUC is the VaD model of 0.596 (95%CI 0.525~0.666).3. through the training set. The accuracy of cross validation model is 71.43%, the accuracy of the test set is 69.42%, the model is stable, and the fitting is good. Tea drinking, hobbies, sleep, age, diabetes, drinking alcohol and so on influence the factors of the Va D classification point variables, among which the root node is the root node. The results of.Logistic regression analysis showed that 3 variables of hobby, drinking tea and sleep were the factors affecting Va D. The prediction accuracy of the model was 64.80%, the prediction accuracy of the test set was 0.714 (95%CI 0.641~0.788) for the 53.92%. decision tree model (95%CI 0.641~0.788), and AUC (95%CI 0.571~0.725) of the Logistic regression model was 0.648 (95%CI 0.571~0.725). The conclusion was 1. in the conclusion. The decision tree model is better than the Logistic regression model.2. excessive drinking, diabetes, hypertension, high fat diet, and insomnia factors are the risk factors for the occurrence of VCI in cerebrovascular disease, and there are hobbies, high education, participation in physical training, and tea drinking is the protective factor of.3. hyperlipidemia, diabetes, and loss. Sleep, high fat diet is a risk factor for the occurrence of VCIND; hobbies, high education, and physical exercise are the protective factors of.4. insomnia, age, diabetes, and drinking are the risk factors for Va D in patients with cerebrovascular disease; tea drinking and hobbies are protective factors.
【學(xué)位授予單位】：青島大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R749.1

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