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| 引用本文: | 郭慧敏.基于关联分析的中老年体检数据的挖掘[J].软件工程,2021,24(5):7-9.【点击复制】 |
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| 摘 要: 根据中老年体检报告，运用Apriori算法挖掘各个指标之间的联系，为医生、患者提供诊断参考与建议。通过安徽省某三甲医院的体检数据，筛选出40岁及以上的中老年人群为研究对象，应用数据挖掘中关联规则的Apriori算法对超重、心电图、脂肪肝、血脂、血压、血糖、尿常规、吸烟、饮酒、总胆固醇等体检指标之间的关联关系进行分析研究。研究表明，体检者的个人不良习惯、超重、高龄、高血糖和脂肪肝等都密切相关，互相影响，提出中老年人群应加强对慢性疾病的预防，保持良好的作息习惯等相关建议。 |
| 关键词: 数据挖掘；关联分析；Apriori算法；中老年体检 |
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中图分类号: TP181
文献标识码: A
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| Data Mining of Physical Examination for the Middle-aged and Elderly based on Association Analysis |
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GUO Huimin
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(School of Economics, Anhui University, Hefei 230601, China)
17755895356@163.com
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| Abstract: This paper proposes to use Apriori algorithm to mine the links between various indicators in the medical examination report of middle-aged and elderly people, which provides diagnosis references and suggestions for doctors and patients. The middle-aged and elderly people aged 40 and above are selected as the research objects from the physical examination data of a Class A tertiary hospital in Anhui Province. Then, Apriori algorithm of association rules in data mining is used to analyze and study the correlation between physical examination indicators, such as overweight, electrocardiogram, fatty liver, blood lipids, blood pressure, blood sugar, urine routine, smoking, drinking, and total cholesterol. Research results show that personal bad habits, overweight, advanced age, high blood sugar, and fatty liver of physical examinees are closely related and affect each other. This paper proposes that middle-aged and elderly people should strengthen the prevention of chronic diseases and maintain good work and rest habits. |
| Keywords: data mining; association analysis; Apriori algorithm; middle-aged and elderly physical examination |
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