CAJ | 학술논문

目的对特发性突聋的风险预测进行数据挖掘,并形成关联规则.方法收集517例特发性突聋患者的临床资料,包括19项特征属性,分别为性别、年龄、季节、高血压、糖尿病、心脏病、高胆固醇血症、动脉粥样硬化、长期抽烟、酗酒、精神紧张、失眠、体质弱、长期卧床、感染、先天性畸形、创伤、肿瘤和自身免疫性疾病.将源数据库进行数据清洗后,映射为挖掘数据库;设置最小支持度为0.1,最小置信度为0.9,进行关联规则分析.结果共形成106个强关联规则,这些强关联规则中蕴含特发性突聋与19项特征属性之间的关联关系.结论本方法有利于将抽象的数理统计理论转变为实用的关联规则来指导疾病预防控制实践.
목적 대특발성돌롱적풍험예측진행수거알굴,병형성관련규칙.방법 수집517례특발성돌롱환자적림상자료,포괄19항특정속성,분별위성별、년령、계절、고혈압、당뇨병、심장병、고담고순혈증、동맥죽양경화、장기추연、후주、정신긴장、실면、체질약、장기와상、감염、선천성기형、창상、종류화자신면역성질병.장원수거고진행수거청세후,영사위알굴수거고;설치최소지지도위0.1,최소치신도위0.9,진행관련규칙분석.결과 공형성106개강관련규칙,저사강관련규칙중온함특발성돌롱여19항특정속성지간적관련관계.결론 본방법유리우장추상적수리통계이론전변위실용적관련규칙래지도질병예방공제실천.
Objective To apply data mining to risk prediction of sudden deafness, and form the association rules.Methods The clinical data of 517 patients with sudden deafness was collected, including the characteristics of 19 attributes: sex, age, season, hypertension, diabetes, heart disease, hypercholesterolemia, atherosclerosis, long-term smoking, alcoholism, mental tension, insomnia, weakness, bedridden, infection, congenital malformation, trauma, tumour and autoimmune diseases. The source database were cleaned, then mapped for mining database. Minimum support to 0.1 and minimum confidence level to 0.9 were set for analysis of association rules. Results One hundred and six strong association rules were formed, and the rules contained the relation between the incidence of sudden deafness and the characteristics of 19 attributes. Conclusion This method is conducive to make the abstract theory of mathematical statistics into useful association rules to guide the practice of disease prevention and control.