本文選題：慢性病管理 + 高血壓　； 參考：《電子科技大學》2017年碩士論文

【摘要】：高血壓患者血壓自我管理意識淡薄,血壓得不到有效控制將引起一系列常見并發(fā)癥。醫(yī)患溝通缺乏會導致醫(yī)生不能及時掌握患者病情發(fā)展,無法開展患者的個性化精準醫(yī)療指導活動。如何使用信息化手段幫助高血壓患者提高血壓自我管理水平,形成良好的醫(yī)患互動模式已成為一個亟待解決的重要問題�；贏ndroid平臺、Visual Studio 2013工具和MySQL數(shù)據(jù)庫設計實現(xiàn)面向高血壓的慢性病管理輔助決策系統(tǒng),服務端采用WCF架構。系統(tǒng)分為患者端和醫(yī)生端,患者端實現(xiàn)血壓心率等體征數(shù)據(jù)采集與監(jiān)控、行為監(jiān)控、高血壓慢性病風險評估和預警等功能;醫(yī)生端實現(xiàn)輔助醫(yī)生對患者進行健康指導功能和制定醫(yī)囑。論文研究內(nèi)容主要包括高血壓慢性病風險因素提取、高血壓慢性病分類診斷、高血壓慢性病風險評估三個模塊。具體研究工作如下:1.引入互信息、遺傳算法(Genetic Algorithm,GA)和樸素貝葉斯(Bayes Na?ve,BN)算法對高血壓慢性病風險因素進行特征提取,互信息是遺傳算法特征選擇的前期準備,NB算法為GA算法中特征子集的評價函數(shù)。與最優(yōu)優(yōu)先搜索(Best First Search,BFS)算法、序列浮動前向選擇(Sequential Floating Forward Selection,SFFS)進行比較。通過特征選擇分類精度分別為87.50%,83.92%,85.71%。2.引入基于加權投票表決的分類器融合算法,基于支持向量機(Support Vector Machine,SVM)、K最近鄰(k-NearestNeighbor,KNN)、樸素貝葉斯(Bayes Na?ve,BN)和反向傳播神經(jīng)網(wǎng)絡(Back-Propagation Neural Networks,BPNN)四個單分類算法進行分類器訓練,通過分類器投票加權和分類排名兩種策略改進算法,基于改進的加權投票表決算法預測準確率提高5%以上。3.引入最小二乘法構建1-1模型,計算單風險因素與高血壓慢性病之間的關系,引用樸素貝葉斯算法構建n-1模型,評估多因素與高血壓慢性病之間的關系。從中提取出與高血壓慢性病有強相關度的因素來進行疾病診斷和風險評估,輔助醫(yī)生進行高血壓慢性病的診斷和治療。
[Abstract]:The self-management consciousness of blood pressure in hypertensive patients is weak and the blood pressure can not be effectively controlled will cause a series of common complications. The lack of communication between doctors and patients will lead to the doctor can not grasp the patient's condition in time, and can not carry out the patient's individualized precise medical guidance. How to use informational means to help the hypertensive patients improve their blood pressure self Management level, forming a good medical and patient interaction model has become an important problem to be solved urgently. Based on the Android platform, Visual Studio 2013 tools and MySQL database design and implement the chronic disease management assistant decision-making system for hypertension. The server adopts the WCF architecture. The system is divided into the patient end and the doctor side, the patient end realizes the blood pressure heart. Rate and other functions, such as data collection and monitoring, behavior monitoring, risk assessment and early warning of hypertension chronic diseases, and doctors to assist doctors to carry out health guidance functions and make medical orders. The main contents of the thesis include the extraction of risk factors of chronic hypertension, the classification diagnosis of high blood pressure slow disease, and the risk assessment of hypertension chronic disease. Three modules are estimated. The specific research work is as follows: 1. introduction of mutual information, genetic algorithm (Genetic Algorithm, GA) and simple Bias (Bayes Na VE, BN) algorithm for characteristic extraction of the risk factors of hypertension chronic disease, mutual information is the pre preparation of the genetic algorithm feature selection, NB algorithm is the evaluation function of the feature subset in the GA algorithm. The first search (Best First Search, BFS) algorithm, sequence floating forward selection (Sequential Floating Forward Selection, SFFS) are compared. The classification accuracy is 87.50%, 83.92%, and 85.71%.2. introduces the classifier fusion algorithm based on the weighted voting, and the nearest neighbor is based on the support vector machine (Support Vector). -NearestNeighbor, KNN), four single classification algorithms of simple Bias (Bayes Na? VE, BN) and back propagation neural network (Back-Propagation Neural Networks, BPNN) are trained for classifier, and two strategies are improved by classifier voting weighting and classification ranking, and the accuracy rate is increased by more than 5%.3 based on the improved weighted voting algorithm. The 1-1 model was constructed with the least square method to calculate the relationship between the single risk factors and the hypertensive chronic disease. The naive Bayes algorithm was used to construct the N-1 model, and the relationship between the multiple factors and the hypertension chronic disease was evaluated. The diagnosis and treatment of high blood pressure chronic disease.

【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R544.1;TP311.52

【參考文獻】

相關期刊論文 前10條

1 林曉斐;;《中國居民營養(yǎng)與慢性病狀況報告(2015年)》發(fā)布[J];中醫(yī)藥管理雜志;2015年13期

2 蹇在金;;說說老年高血壓的特點[J];保健醫(yī)苑;2013年10期

3 葛衛(wèi)紅;謝菡;;慢病管理現(xiàn)狀[J];藥學與臨床研究;2012年06期

4 謝紅薇;林玲玲;;C4.5算法對高血壓病分類規(guī)則提取的研究[J];電腦開發(fā)與應用;2012年06期

5 劉力生;;中國高血壓防治指南2010[J];中華高血壓雜志;2011年08期

6 袁濤;薛夢;黃曉波;曾偉;劉雅;易延靜;楊雷;鐘宏;胡詠梅;聶曉莉;賈勇;冉迅;;成都地區(qū)老年人群高血壓的流行病學調(diào)查[J];四川醫(yī)學;2011年04期

7 呂冬姣;張玨;王霄英;郭雪梅;王翠艷;;人工神經(jīng)網(wǎng)絡在前列腺癌診斷中的應用[J];北京大學學報(醫(yī)學版);2009年04期

8 馬光志;盧炎生;宋恩民;聶紹發(fā);靖偉峰;張^，

本文編號：1798698