使用關聯性分析探勘生活習慣之 改變與血液化學檢測變化之關係

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：80

、訪客IP：18.191.236.174

姓名

曹洧浪(Wei-Lang Tsao) 查詢紙本館藏

畢業系所

系統生物與生物資訊研究所

論文名稱

使用關聯性分析探勘生活習慣之改變與血液化學檢測變化之關係
(Discovering the relationships between the changes of lifestyles and blood chemistry differences using association rule mining.)

相關論文

★ 細菌物種基因體中非編碼小片段核糖核酸之預測	★ 從年齡動態網路探討疾病盛行率
★ 藉由比較基因表現資料研究次世代定序與晶片技術分析差異	★ 啟動子甲基化與對應之基因表現微陣列資訊整合分析
★ 乾燥綜合症與非病毒型肝炎之相關因子分析	★ 氣候變遷對人類疾病網路造成衝擊
★ 台北和中壢地區不孕症分佈與共病探討	★ 探討台灣的門診疾病與環境空氣品質的濃度變化之相關性
★ 以地區醫院病例探討桃園之地域族群與疾病之差別	★ 桃園地區之區域與疾病盛行率之關聯
★ CyTOF之生物標記篩選與分析	★ 透明細胞腎細胞癌質譜流式細胞儀資料分析與視覺化
★ 使用支持向量機預測蛋白質醣基化位置	★ 使用基因表現資料預測基因轉錄調控網路
★ RNA Riboswitch搜尋系統之設計與實作	★ 人類疾病差異表現基因與調控網路之整合系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

飲食及運動對於許多疾病具有直接或間接的影響。例如代謝症
候群、糖尿病以及心血管疾病。而這些影響會在血液檢測中得到顯現。
例如高密度脂蛋白會降低得到心血管疾病的風險。本研究旨在發現飲
食習慣以及運動習慣變化與血液檢測數值變化的關聯性。
本研究利用聯新國際醫院自 2006 年到 2011 年收集的資料。資
料收取 30 歲以上的群體，我們使用的變項包含 21 種身體檢查數值、
10 種飲食習慣、有無運動習慣、性別以及年齡。我們將具有複數次檢
查結果的群體找出，並合併得出生活習慣變化以及檢查數值變化幅度。
我們使用一種機器學習技術，稱為關聯性分析，來尋找生活習慣改變
與身體檢測數值的關聯性。
在女性中我們找到 21 條規則。影響較多的生活習慣是喝咖啡有
3 條、喝茶有 5 條、有運動 4 條。在男性中我們找到 20 條規則，影
響較大的生活習慣則是吃點心有 4 條，喝豆漿有 4 條
咖啡與總膽固醇上升成正相關在女性。多喝茶可以促進高密度
脂蛋白上升來降低得到心血管疾病的風險，運動對於減重及降低心血
管疾病風險因子有關。在男性方面。吃點心會增進心血管疾病因子上
升，豆漿飲用量減少會導致膽固醇下降。

摘要(英)

Diet and exercise have a direct or indirect effect on many diseases.
Examples include Metabolic syndrome (Mets), diabetes mellitus type 2
(DMT2), and cardiovascular atherosclerotic diseases (CVD). These effects
can be seen in blood chemistry tests. This study aimed to find the
association between changes in diet and exercise and changes in blood
chemistry test values.
Our data was collected by Landseed international hospital from 2006
to 2011. We used 21 physical examination values, 10 dietary habits,
exercise, gender, and age. We identified groups with multiple test results
and combined them to derive changes in lifestyle and test values. We use a
machine learning technique called association rule mining to find the
association between lifestyle changes and body test values.
The result shows 21 rules in females. The more influential lifestyles
are Coffee (3/21), Tea (5/21), and Exercise (4/21). Among men, The result
shows 20 rules in males. The more influential lifestyle are dessert (4/20),
soymilk (4/20) exercise (3/20).
Coffee is positively associated with CHO rise (CVD risk factor) in
women. Drinking more tea can promote the rise of HDL to reduce the risk
of getting CVD, and exercise is related to weight loss and lower CVD risk
factors. In men, Dessert increases CVD risk factors, and reduced soymilk
consumption leads to a decrease in CHO and an increase in UA. Soymilk
is negatively associated with GPT (liver function index). Stopping exercise
increased BMI and CHO (CVD risk factor)

關鍵字(中)

★ 關聯性分析
★ 血液化學檢測

關鍵字(英)

★ association rule mining
★ blood chemistry

論文目次

Contents
中文摘要.........................................................................................i
Abstract .........................................................................................ii
誌謝.............................................................................................. iii
Contents........................................................................................iv
Contents of table ..........................................................................vi
Contents of Figure......................................................................viii
1. Introduction..........................................................................1
1.1 Disease and blood chemistry values...................................1
1.2 lifestyle and disease ...........................................................1
1.2.1 Diet ............................................................................................ 1
1.2.2 Exercise...................................................................................... 1
1.2.3 Smoke ........................................................................................ 1
1.2.4 Alcohol....................................................................................... 2
1.3 Motivation .........................................................................2
1.4 Goal...................................................................................2
2. Materials and Methods........................................................4
2.1 Process flow ......................................................................4
2.2 Data source........................................................................6
2.3 Data preprocessing ............................................................6
2.3.1 Feature ....................................................................................... 6
2.3.2 Data cleaning.............................................................................. 8
2.3.3 Data combination ....................................................................... 8
2.3.4 lifestyle change........................................................................... 8
2.3.5 Blood chemistry value trend ....................................................... 9
2.4 Association rule mining .....................................................9
v
2.4.1 Apriori algorithm...................................................................... 10
2.5 Python3 ...........................................................................10
2.5.1 SciPy........................................................................................ 10
2.5.2 Mlxtend.................................................................................... 10
3. Result ..................................................................................12
3.1 Lifestyle’s effect of blood chemistry................................12
3.1.1 Female...................................................................................... 12
3.1.2 Male ......................................................................................... 14
3.2 Data distribution after combination..................................15
3.2.1 Distribution of lifestyle change................................................. 15
3.2.2 Distribution of blood chemistry value trend .............................. 18
3.3 ARM result......................................................................20
3.3.1 Female...................................................................................... 20
3.3.2 Male ......................................................................................... 22
4. Discussion ...........................................................................24
4.1 Small number of single lifestyle change group ................24
4.2 The difference between the results of lifestyle and lifestyle
change 24
4.3 Additives and types of coffee and tea...............................24
4.4 Same lifestyle has different effect in gender ....................25
4.5 Possible in future .............................................................25
Reference .....................................................................................27
Appendix .....................................................................................30

參考文獻

1. Ference, B.A., et al., Low-density lipoproteins
cause atherosclerotic cardiovascular disease. 1. Evidence
from genetic, epidemiologic, and clinical studies. A
consensus statement from the European Atherosclerosis
Society Consensus Panel. Eur Heart J, 2017. 38(32): p.
2459-2472.
2. Vijan, S., In the clinic. Type 2 diabetes. Ann Intern
Med, 2010. 152(5): p. ITC31-15; quiz ITC316.
3. Widmer, R.J., et al., The Mediterranean diet, its
components, and cardiovascular disease. Am J Med, 2015.
128(3): p. 229-38.
4. van Dam, R.M. and E.J. Feskens, Coffee
consumption and risk of type 2 diabetes mellitus. Lancet,
2002. 360(9344): p. 1477-8.
5. Chang, H.C., et al., Changes in High-Density
Lipoprotein Cholesterol Levels in Relation to Coffee
Consumption Among Taiwanese Adults. J Multidiscip
Healthc, 2020. 13: p. 1427-1432.
6. Onuegbu, A.J., et al., Consumption of soymilk
lowers atherogenic lipid fraction in healthy individuals. J
Med Food, 2011. 14(3): p. 257-60.
7. Warburton, D.E., C.W. Nicol, and S.S. Bredin,
Health benefits of physical activity: the evidence. Cmaj,
2006. 174(6): p. 801-9.
8. Villella, M. and A. Villella, Exercise and
cardiovascular diseases. Kidney Blood Press Res, 2014.
39(2-3): p. 147-53.
9. Lumb, A., Diabetes and exercise. Clin Med
(Lond), 2014. 14(6): p. 673-6.
10. van der Windt, D.J., et al., The Effects of Physical
Exercise on Fatty Liver Disease. Gene Expr, 2018. 18(2): p.
89-101.
11. Cao, M. and W. Chen, Epidemiology of lung
cancer in China. Thorac Cancer, 2019. 10(1): p. 3-7.
12. Price, J., et al., Relationship between smoking and
28
cardiovascular risk factors in the development of peripheral
arterial disease and coronary artery disease; Edinburgh
Artery Study: Edinburgh Artery Study. European heart
journal, 1999. 20(5): p. 344-353.
13. Jonas, M.A., et al., Statement on smoking and
cardiovascular disease for health care professionals.
American Heart Association. Circulation, 1992. 86(5): p.
1664-1669.
14. Bolinder, G., et al., Smokeless tobacco use and
increased cardiovascular mortality among Swedish
construction workers. American Journal of Public Health,
1994. 84(3): p. 399-404.
15. Negri, E., et al., Tar yield of cigarettes and risk of
acute myocardial infarction. GISSI-EFRIM Investigators.
British Medical Journal, 1993. 306(6892): p. 1567-1570.
16. Singal, A.K. and B.S. Anand, Recent trends in the
epidemiology of alcoholic liver disease. Clinical Liver
Disease, 2013. 2(2): p. 53.
17. Mann, R.E., R.G. Smart, and R. Govoni, The
epidemiology of alcoholic liver disease. Alcohol research &
health, 2003. 27(3): p. 209.
18. Mandayam, S., M.M. Jamal, and T.R. Morgan.
Epidemiology of alcoholic liver disease. in Seminars in liver
disease. 2004. Published in 2004 by Thieme Medical
Publishers, Inc., 333 Seventh Avenue ….
19. Mendenhall, C.L., Anabolic steroid therapy as an
adjunct to diet in alcoholic hepatic steatosis. The American
journal of digestive diseases, 1968. 13(9): p. 783-791.
20. Frawley, W.J., G. Piatetsky-Shapiro, and C.J.
Matheus, Knowledge Discovery in Databases: An Overview.
AI Magazine, 1992. 13(3): p. 57.
21. Hahsler, M., B. Grün, and K. Hornik, arules-A
computational environment for mining association rules
and frequent item sets. Journal of statistical software, 2005.
14: p. 1-25.
22. Virtanen, P., et al., SciPy 1.0: fundamental
algorithms for scientific computing in Python. Nature
29
Methods, 2020. 17(3): p. 261-272.
23. Raschka, S., MLxtend: Providing machine
learning and data science utilities and extensions to
Python′s scientific computing stack. Journal of Open Source
Software, 2018. 3(24): p. 638.
24. Kondo, Y., et al., Effects of Coffee and Tea
Consumption on Glucose Metabolism: A Systematic Review
and Network Meta-Analysis. Nutrients, 2018. 11(1).
25. Cornelis, M.C. and R.M. van Dam, Habitual
Coffee and Tea Consumption and Cardiometabolic
Biomarkers in the UK Biobank: The Role of Beverage Types
and Genetic Variation. J Nutr, 2020. 150(10): p. 2772-2788.
26. Nery, C., et al., Effectiveness of resistance exercise
compared to aerobic exercise without insulin therapy in
patients with type 2 diabetes mellitus: a meta-analysis. Braz
J Phys Ther, 2017. 21(6): p. 400-415.
27. Nassef, Y., et al., The Impact of Aerobic Exercise
and Badminton on HDL Cholesterol Levels in Taiwanese
Adults. Nutrients, 2020. 12(5

指導教授

吳立青(Wu, Li-Ching)

審核日期

2022-9-22

推文