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

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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.)

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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

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指導教授

吳立青(Wu, Li-Ching)

審核日期

2022-9-22

推文