中国全科医学
中國全科醫學
중국전과의학
Chinese General Practice
2015年
25期
3050-3053,3058
,共5页
曹文君%徐勇勇%谭志军%王庸晋
曹文君%徐勇勇%譚誌軍%王庸晉
조문군%서용용%담지군%왕용진
人工神经网络%慢性病%危险因素
人工神經網絡%慢性病%危險因素
인공신경망락%만성병%위험인소
Artificial neural network%Chronic disease%Risk factors
目的:探讨基于人工神经网络(ANN)模型的多个慢性病主要危险因素筛查。方法选取2008年1月—2010年12月参加北京某健康管理中心体检的年龄45岁及以上人群6938例。采用逐步回归和遗传算法相结合的方法确定 ANN 输入变量,尝试构建高血压、糖尿病、冠心病及慢性病患者预测模型,并采用受试者工作特征(ROC)曲线评价预测模型的准确性。结果6938例体检人群中高血压患者1665例(24.0%),糖尿病患者609例(8.8%),冠心病患者443例(6.4%)。年龄、体质指数、胸围、腰臀比、总胆固醇、高密度脂蛋白胆固醇、尿酸、性别、尿糖、高血压家族史、糖尿病家族史、心血管疾病家族史是慢性病患者的主要危险因素,其中以年龄对慢性病患病的影响最大,作用效应为25.3%。高血压、糖尿病、冠心病及慢性病 ANN 预测模型 ROC 曲线下面积分别为0.80、0.87、0.81、0.78,预测高血压、糖尿病、冠心病、患任一慢性病的准确性分别为75.1%、91.2%、93.7%、75.2%。结论利用ANN 模型筛选出多个慢性病主要危险因素,可为慢性病的有效预防提供科学依据。
目的:探討基于人工神經網絡(ANN)模型的多箇慢性病主要危險因素篩查。方法選取2008年1月—2010年12月參加北京某健康管理中心體檢的年齡45歲及以上人群6938例。採用逐步迴歸和遺傳算法相結閤的方法確定 ANN 輸入變量,嘗試構建高血壓、糖尿病、冠心病及慢性病患者預測模型,併採用受試者工作特徵(ROC)麯線評價預測模型的準確性。結果6938例體檢人群中高血壓患者1665例(24.0%),糖尿病患者609例(8.8%),冠心病患者443例(6.4%)。年齡、體質指數、胸圍、腰臀比、總膽固醇、高密度脂蛋白膽固醇、尿痠、性彆、尿糖、高血壓傢族史、糖尿病傢族史、心血管疾病傢族史是慢性病患者的主要危險因素,其中以年齡對慢性病患病的影響最大,作用效應為25.3%。高血壓、糖尿病、冠心病及慢性病 ANN 預測模型 ROC 麯線下麵積分彆為0.80、0.87、0.81、0.78,預測高血壓、糖尿病、冠心病、患任一慢性病的準確性分彆為75.1%、91.2%、93.7%、75.2%。結論利用ANN 模型篩選齣多箇慢性病主要危險因素,可為慢性病的有效預防提供科學依據。
목적:탐토기우인공신경망락(ANN)모형적다개만성병주요위험인소사사。방법선취2008년1월—2010년12월삼가북경모건강관리중심체검적년령45세급이상인군6938례。채용축보회귀화유전산법상결합적방법학정 ANN 수입변량,상시구건고혈압、당뇨병、관심병급만성병환자예측모형,병채용수시자공작특정(ROC)곡선평개예측모형적준학성。결과6938례체검인군중고혈압환자1665례(24.0%),당뇨병환자609례(8.8%),관심병환자443례(6.4%)。년령、체질지수、흉위、요둔비、총담고순、고밀도지단백담고순、뇨산、성별、뇨당、고혈압가족사、당뇨병가족사、심혈관질병가족사시만성병환자적주요위험인소,기중이년령대만성병환병적영향최대,작용효응위25.3%。고혈압、당뇨병、관심병급만성병 ANN 예측모형 ROC 곡선하면적분별위0.80、0.87、0.81、0.78,예측고혈압、당뇨병、관심병、환임일만성병적준학성분별위75.1%、91.2%、93.7%、75.2%。결론이용ANN 모형사선출다개만성병주요위험인소,가위만성병적유효예방제공과학의거。
Objective To discuss the identification of major risk factors for multiple chronic diseases based on artificial neural network(ANN). Methods We enrolled 6 938 subjects aged 45 or older than 45 who received physical examination in a health management center in Beijing from January 2008 to December 2010. Stepwise regression combined with genetic algorithm was used to determine the input variables of artificial neural network( ANN). We tried to build the prediction models for hypertension,diabetes mellitus,coronary heart disease and chronic diseases and then evaluated the accuracy of these models by receiver operator characteristic( ROC)curve. Results Among 6 938 subjects,1 665 ( 24. 0% )had hypertension,609 (8. 8% ) had diabetes mellitus, and 443 ( 6. 4% ) had coronary heart disease. Age, body mass index ( BMI ), chest circumference,waist - hip ratio,total cholesterol,HDL - C,uric acid,gender,urine sugar,family history of hypertension, family history of diabetes mellitus and family history of cardiovascular disease are major risk factors for chronic diseases,among which age had the greatest influence on chronic diseases with an effect rate of 25. 3%. Moreover,the areas under ROC curves of ANN prediction models for blood pressure,diabetes,coronary heart disease and chronic disease were 0. 80,0. 87,0. 81 and 0. 78 respectively. The accuracy rates in the prediction for hypertension,diabetes,coronary disease and chronic disease were 75. 1% ,91. 2% ,93. 7% and 75. 2%. Conclusion Main risk factors for multiple chronic diseases could be identified by ANN model,which could provide scientific references for effective prevention of chronic diseases.