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1 데이터마이닝을활용한뇌경색증과동반되는질환의연관성분석 The Journal Korean Society of Physical Therapy 이인희, 신아미, 손창식, 박희준, 김중휘 1, 박상영 2, 최진호 3, 김윤년 4 계명대학교의과대학의료정보학교실 ( 계명대학교동산병원물리치료실 ), 계명대학교의과대학의료정보학교실 ( 계명대학교보건의료정보기술연구소 ), 계명대학교의과대학의료정보학교실, 1 강병원물리치료실, 2 남산병원물리치료실, 3 대구한의대학교보건치료대학물리치료학과, 4 계명대학교의과대학내과학교실 ( 계명대학교동산병원심장내과 ) Association Analysis of Comorbidity of Cerebral Infarction Using Data Mining In-Hee Lee, PT, MSc; A-Mi Shin, RN, MSc; Chang-Sik Son, Post PhD; Hee-Joon Park, PhD; Joong-Hwi Kim, PT, PhD; 1 Sang-Young Park, PT, MSc; 2 Jin-Ho Choi, PT, PhD; 3 Yoon-Nyun Kim, MD, PhD 4 Department of Medical Informatics, School of Medicine, Keimyeung University; 1 Department of Physical Therapy, Kang Hospital; 2 Department of Physical Therapy, NamSan Hospital; 3 Department of Physical Therapy, College of Health and Therapy, Daegu Haany University; 4 Department of Internal Medicine, School of Medicine, Keimyeung University Purpose: The purpose of this study was to apply association rule mining to explore the labyrinthine network of cerebral infarction comorbidity and basic data supply to develop cutting-edge physical therapy protocols for cerebral infarction with comorbidity Methods: From clinic records of enrollees of A Hospital in D city, patients over 18 years of age with cerebral infarction and cerebral infarction comorbidity were recruited as a case group. All diagnoses of that hospital were categorized according to the International Classification of Disease (ICD) diagnosis system. We extracted code I63 from the Korea Classification of Disease (KCD)-4. Associated rule mining was done with a priori modeling and Web nodes to examine the strengths of associations among those diagnoses. The support and confidence values of associated rule mining results were examined. Results: The subjects of this study were 2,267 cerebral infarction patients. E11 (Non-insulin-dependent diabetes mellitus), E78 (Disorders of lipoprotein metabolism and other lipidaemias), G81 (Hemiplegia), I10 (Essential hypertension), and K29 (Gastritis and duodenitis) were high frequency diagnoses, being found in 10% or more of total diagnoses of cerebral infarction from frequency analysis results. The highest frequency diagnosis was 1,042 (46.0%) for I10. The second most frequent diagnosis was for E11(21.5%) while the third most frequent diagnosis was E78 (20.2%). Results from a priori modeling and Web nodes indicated that cerebral infarction has a strong association withessential hypertension, non-insulin-dependent diabetes mellitus, disorders of lipoprotein metabolism and other lipidaemias. Conclusion: Cerebral infarction is associated with hypertension, diabetes mellitus, and disorders of lipoprotein metabolism and other lipidaemias. The result of this study will be helpful to clinicians treating patients with cerebral infarction. Keywords: A priori modeling, Cerebral infarction, Essential hypertension 논문접수일 : 2009 년 12 월 5 일수정접수일 : 2010 년 2 월 1 일게재승인일 : 2010 년 2 월 8 일교신저자 : 신아미, ami535@kmu.ac.kr 데이터마이닝을활용한뇌경색증과동반되는질환의연관성분석 75

2 J Kor Soc Phys Ther 2010;22(1):75-81 I. 서론뇌혈관질환은우리나라 3대사망원인중하나로악성신생물다음으로사망률이높고나이가들수록사망률이높다. 순환기계통질환중에서는뇌혈관질환 ( 인구 10만명당 56.5명 ), 심장질환 ( 인구 10만명당 43.4명 ) 순으로사망률이높다. 1 뇌혈관질환중가장흔히볼수있는뇌졸중은허혈성뇌졸중 ( 뇌경색증 ) 과출혈성뇌졸중으로분류할수있다. 허혈성뇌졸중은혈전이나색전이뇌혈관을막을때발생하고, 출혈성뇌졸중은뇌혈관이갑자기터져발생하는것으로혈액이뇌세포속으로스며들게된다. 2,3 출혈성뇌졸중이감소하고있는반면뇌경색증은증가추세에있는데 Yu 등 4 은 Korean Stroke Registry 등록된전국 29개대형병원에서 2002년 11월부터 2004년 6월까지전향적으로자료수집한 12,072명의급성기뇌졸중환자에대해분석한결과뇌경색증이 10,811명 (89.6%) 으로가장많았고, 출혈성뇌졸중이 667명 (5.5%), 일과성허혈발작이 5945명 (4.9%) 있었다고보고하였다. 뇌경색증은병변에상응하는뇌영역에갑작스런신경학적결손이야기되고편마비, 감각장애, 운동조화및균형장애, 인지기능장애, 언어장애등이유발한다. 5 여러연구를통해뇌졸중은 50% 이상에서신체적, 기능적장애를초래하며, 첫발병후 1달이내사망률이 22% 이고, 10년이내사망하거나지속적인치료가요구되는경우는 79% 에이르는것으로나타났다. 따라서뇌경색증환자들은활동능력이제한됨으로써최대한의기능회복을위해상당한기간에걸친물리치료및재활치료가요구된다 뇌경색증의위험요인으로고혈압, 당뇨, 심장질환, 혈중콜레스테롤수치, 흡연, 스트레스, 과체중 ( 전신, 복부 ), 음주, 운동부족등이있다. 12 Yang 등 13 은뇌졸중의중요한위험인자의빈도는고혈압, 뇌졸중기왕력, 심장질환, 고콜레스테롤혈증, 당뇨병의순이었고, 뇌경색과뇌출혈을비교할때뇌경색군에서 3개이상의위험인자를중복해서갖는빈도가높게나타났다고보고하였다. 뇌졸중의위험요인과관련된연구를통해뇌졸중과관련된여러질환이밝혀졌으나이는모두뇌졸중과관련된질환사이의상관관계혹은동시발생률에대한것으로뇌졸중과관련된셋이상의여러질환들사이의연관성에대한분석이필요하다. 연관규칙마이닝 (association rule mining) 은데이터안에존재하는품목간의연관규칙 (association rule) 을발견하는과정이다. 연관규칙마이닝은유통업에서의판매시점의거래내역을분석하는것에서시작되었지만유통업이외에환자의의무기록에서여러치료가같이이루어진경우합병증발생의징후를알아보는데유용하다. 14 연관규칙마이닝을의학분야에최근접목을시도한 Tai와 Chiu 15 는연관규칙마이닝을셋혹 은그이상의항목사이의연관성을확인하는데사용하였고, 연관규칙에서나타난신뢰도 (confidence) 는역학에서동반질환 (comorbidity) 과수학적으로같은의미라고하였으며, 연관규칙마이닝중아프리오리모델링 (Apriori modeling) 을이용하여주의력부족행동과다장애 (attention deficit hyperactivity disorder, ADHD) 와동반되는정신과적질환사이의연관성을제시하였다. 뇌경색증은여러선행연구를통해고혈압, 당뇨와관련이깊고, 심혈관계와뇌혈관계질환의재발위험또한높은것으로알려져있고, 16 이러한질환들은뇌경색증환자의신체적기능회복에영향을미치고, 물리치료시환자의기능결손정도뿐만아니라후행으로동반된질환을고려하여환자에게맞는물리치료를제공하는것이필요할것으로생각된다. 본연구의목적은실증적임상의자료분석을통하여뇌경색증과흔히동반되는질환을파악하고, 연관규칙마이닝을활용하여셋이상의여러질환들사이의연관성을분석함으로써뇌경색증과함께다른질환이동반된경우의대상자에게적절한물리치료를제공하는데필요한기초자료를제공하고자하였다. II. 연구방법 1. 연구진행절차뇌경색증과동반된질환사이의연관성분석과정은 Figure 1 과같다. 대상의료기관의 D/W (data warehouse) 에서뇌경색증환자가진단받은동반질환을추출한후데이터정제과정을거쳐데이터마트를구축하였다. 연관규칙마이닝기법중아프리오리모델링을적용하여연관규칙을생성하고그중의미있는규칙을찾아낼수있었다. 연관규칙마이닝은수백만개의여러데이터들간의관련성을찾아내기위하여여러조합과집합을산정하고, 이들을확률의표현으로나타내는것으로항목집합 A가나타날때항목집합 B도함께나타나는경향이있음을의미한다. 2. 연구대상및시기, 자료수집방법본연구에서는 D광역시에위치한 A의료원에뇌경색증으로입원한환자의진단데이터를이용하였다. 그중 2000년에서 2007년사이에입원한환자중주진단이뇌경색증인만 18세이상의 2,267명의환자진단데이터를대상으로하였다. 자료수집을위해 Clementine 12.0 프로그램을이용하였고, A 의료원의 D/W에서국제질병사인 (International Classification of Disease, ICD) 분류코드중진단명이뇌경색증 (Cerebral infarction) 이고, Korea classification of disease (KCD)-4 코드 76 Association Analysis of Comorbidity of Cerebral Infarction Using Data Mining

3 Figure 1. Process of analysis using data mining. I63을진단받은환자의모든진단정보를추출하였다. 진단자료추출시뇌경색증진단시점을중심으로진단전 후를구별하지않고뇌경색증을진단받은환자의모든진단정보를추출하였으며이름, 주민등록번호, 연락처등과같은대상자의인적사항은제외하였다. 3. 뇌경색증환자진단데이터마트구축 2000년에서 2007년사이 A의료원에서뇌경색증을진단받은대상자는총 2,267명의대상자에게내려진진단건수는총 32,692건이었다. 한환자가동일한질환으로여러번진단받은경우중복데이터로인해특정질환의지지도가높게나타날수있으므로등록번호와진단코드가같은중복데이터는제거하였다. 진단코드중외적요인과관련된진단코드인손상, 중독및외인에의한특정기타결과 (S00-T98), 질병이환및사망의외인 (V01-Y98), 건강상태및보건서비스접촉에영향을주는요인 (Z00-Z99), 특수목적코드 (U00-U99) 는제외하였다. 또, 효율적인분석을위해진단건수가 5% 이하인진단도제외하였다. 중복데이터및외적요인과관련된진단코드, 진단건수가 5% 미만인진단코드를제외한총 6,695건에대한데이터마트를구축하였고이를최종분석에이용하였다. Chiu, 2009), 품목코딩이이항형이나다항형인경우적용가능하다. 아프리오리모델링결과나타난규칙은지지도 (Support) 와신뢰도 (Confidence) 로평가한다. 지지도는전체질환에서질환 A와질환 B가함께발생한건수로지지도가높다는것은질환이동시에많이진단됨을의미한다. 신뢰도는질환 A가발생한건수중질환 B가함께발생한건수를나타내는것으로신뢰도가높다는것은선행질환이진단된경우후발질환이많이진단됨을의미한다. 이들에대한공식은다음과같다 Support(%)= Confidence(%)= Number of disease A B Total' number of disease Number of disease A B Total' number of disease A 3) 연관관계가있는질환들이묶이는양상을그래프로나타내기위해웹노드 (Web node, 거미줄망 ) 를이용하였다. 웹노드는변수간의연관성을탐색할때주로이용되는그래프로연관규칙마이닝에서유용하게사용된다. 연결강도가강할수록그래프상에서굵게표시된다 자료분석자료분석을위해 SPSS 12.0 program과 SPSS Clementine 12.0 프로그램을이용하였고, 구체적인분석방법은다음과같다. 1) 대상자의성별, 뇌경색증과동반된질환의분포는빈도와백분율을구하였고, 성별에따른질환분포에차이가있는지를알아보기위해카이제곱검증을실시하였다. 2) 뇌경색증과동반된질환사이의연관성을분석하기위해아프리오리모델링을적용하였다. 연관규칙마이닝의여러분석기법중아프리오리모델링은동반된질병사이의연관성을분석하는데적절하고 (Tai와 III. 결과 1. 대상자의성별및연령대상자의성별은남자가 1,434명 (63.3%) 이고여자가 833명 (36.7%) 로남자가더많았다. 연령은평균 50.67±7.38세였다. 2. 뇌경색증과동반된질환분포뇌경색증과동반된질환을분석한결과 E11( 인슐린- 비의존성당뇨병 ), E78( 지단백질대사장애및기타지혈증 ), G81( 편마비 ), I10( 본태성고혈압 ), K29( 위염및십이지장염 ) 는전체진단건수의 10% 이상을차지하는다빈도진단이었다. 그중 I10 데이터마이닝을활용한뇌경색증과동반되는질환의연관성분석 77

4 J Kor Soc Phys Ther 2010;22(1):75-81 이 1,042건 (46.0%) 으로가장많았고, 그다음으로 E11 (21.5%), E78(20.2%) 순으로높은빈도를나타내었다. 성별에따른진단의분포에서도남녀모두 I10, E11, E78 순으로많았고, 두그룹사이에통계적으로유의한차이가있는진단은없었다 (Table 1). Table 1. High frequency diagnoses in cerebral infarction (N=2,267) DX code Disease E11 Non-insulin-dependent diabetes mellitus E78 Disorders of lipoprotein metabolism and other lipidaemias Transient cerebral G45 ischaemic attacks and related syndromes Vascular syndromes of G46 brain in cerebrovascular diseases G81 Hemiplegia Disorders of vestibular H81 function I10 Essential hypertension I20 Angina pectoris I48 I65 I69 Atrial fibrillation and flutter Occlusion and stenosis of precerebral arteries, not resulting in cerebral infarction Sequelae of cerebrovascular disease K25 Gastric ulcer K29 Gastritis and duodenitis K76 Other diseases of liver Total Male Female N (%) N (%) N (%) 488 (21.5) 459 (20.2) 142 (6.3) 210 (9.3) 287 (12.7) 195 (8.6) 1,042 (46.0) 146 (6.4) 120 (5.3) 119 (5.2) 158 (7.0) 138 (6.1) 256 (11.3) 128 (5.6) 311 (21.7) 292 (20.4) 89 (6.2) 136 (9.5) 173 (12.1) 123 (8.6) 665 (46.4) 92 (6.4) 75 (5.2) 82 (5.7) 108 (7.5) 88 (6.1) 155 (10.8) 78 (5.4) 177 (21.2) 167 (20.0) 53 (6.4) 74 (8.9) 114 (13.7) 72 (8.6) 377 (45.3) 54 (6.5) 45 (5.4) 37 (4.4) 50 (6.0) 50 (6.0) 101 (12.1) 50 (6.0) 3. 아프리오리모델링에의한연관성분석결과 χ2 p 아프리오리모델링을적용한결과뇌경색증환자에게내려진진단의연관규칙은 Table 2와같다. Treshold값은지지도 10% 이상, 신뢰도 95% 이상으로설정하였고이를만족시키는규칙은총 7가지로나타났다. 그중에서 I10( 본태성고혈압 ) 과 I63( 뇌경색증 ) 이동반된경우지지도가 45.9% 로모든규칙중 가장높게나타났다. E11( 인슐린 -비의존성당뇨병 ) 과 I63이동반된경우와 E78과 I63이동반된경우는지지도가 20% 이상으로나타난규칙이었다. E78, I10, I63이동반된경우와 E11, I10, I63이동반된경우지지도가각각 13.2%, 13.7% 로나타났다. 모든규칙에서신뢰도는 % 로매우높게나타났다. Table 2. The result of apriori modeling application Consequent Antecedent Support (%) Confidence (%) Lift I63 E I63 E I63 E78, I I63 E11, I I63 I I63 G I63 K E11: Non-insulin-dependent diabetes mellitus, E78: Disorders of lipoprotein metabolism and other lipidaemias, G81: Hemiplegia, I10: Essential hypertension, I63: Cerebral infarction, K29: Gastritis and duodenitis 4. 웹노드를이용한그래프생성결과 뇌경색증과동반된질환사이의연관성을시각화하기위해웹노드를이용한그래프생성결과는 Figure 2와같다. 연관성이있는질환들은선으로연결되고연관성이높을수록연결선이굵게표시된다. I63( 뇌경색증 ) 은 I10( 본태성고혈압 ), H81( 전정기능의장애 ), G81( 편마비 ), G46( 대뇌혈관질환에서의뇌혈관증후군 ), E78( 지단백질대사장애및기타지혈증 ), E11( 인슐린-비의존당뇨병 ), K29( 위염및십이지장염 ), I69( 뇌혈관질환의후유증 ) 와연관되는것으로나타났지만 I10과연관성이가장높았다. I63, I10, E11, E78은서로연결되어상호연관되는질환인것으로나타났다. IV. 고찰 본연구는데이터마이닝의연관성분석기법중아프리오리모델링을이용하여뇌경색증과동반되는여러질환들사이의연관성을분석하기위해시도되었다. 본연구에서는전산화된의무기록데이터를이용함으로써 2,267명의뇌경색증환자로부터대용량의진단정보를단시간에추출할수있었고, 이는과거종이의무기록검토를통한역학적연구나내원환자를대상으로자료수집을실시한전향적연구와는차이가있다. 또한, 다량의데이터를기반으로데이터마이닝분석기법중아프리오리모델링을적용하여뇌경색증과동반되는셋이상의질환들사이의연관성을분석했다는데의의가있다. 78 Association Analysis of Comorbidity of Cerebral Infarction Using Data Mining

5 Figure 2. A link diagram from association rule mining result graphed by Web node of SPSS Clementine program. E11: Non-insulin-dependent diabetes mellitus, E78: Disorders of lipoprotein metabolism and other lipidaemias, H81: Disorders of vestibular function, G46: Vascular syndromes of brain in cerebrovascular diseases, G81: Hemiplegia, I10: Essential hypertension, I63: Cerebral infarction, I69: Sequelae of cerebrovascular disease, K29: Gastritis and duodenitis. 고혈압은뇌졸중의가장중요한위험인자로뇌경색증환자의 50% 이상, 뇌출혈환자의 70 80% 에서고혈압이동반된다. 20 본연구에서도아프리오리모델링결과에서도뇌경색증과본태성고혈압의연관규칙이신뢰도가가장높게나타났고, 웹노드를이용한그래프생성결과뇌경색증과본태성고혈압이가장연관성이높은것으로나타나고혈압이뇌경색증과관련된중요한질환임을확인할수있었다. 당뇨병은뇌졸중의직 간접원인으로당뇨병환자는고지혈증및동맥경화증이잘생기고, 출혈성뇌졸중보다는허혈성뇌졸중의위험인자로당뇨병이있는남자에서 1.8배, 여자에서 2.2배정도뇌졸중의위험도가높은것으로알려져있다 또, Lee 25 는고혈압이있는출혈성뇌졸중혼자 31명, 허헐성뇌졸중환자 117명을대상으로후향적조사를실시한결과고혈압이있는뇌졸중환자에서당뇨병은허헐성뇌졸중의위험도를높이는질환으로뇌혈관협착정도와관계가높다고보고하였다. 연구방법에있어차이가있지만본연구에서도뇌경색증은고혈압뿐만아니라인슐린 -비의존당뇨병, 지단백질대사장애및기타지혈증과연관성이높은질환으로나타났다. 특히웹노드를이용한그래프생성결과에서는뇌경색증, 본태성고혈압, 인슐린 -비의존당뇨병, 지단백질대사장애및기타지혈증네질환이서로연관되는것으로나타났다. Yu 등 4 은한국뇌졸중자료은행에등록된 10,11명의급성기허헐성뇌졸중환자를분석한결과입원당시고혈압을보였던환자의 89.9% 가기존에고혈압을진단받았으나, 이들중정기적인치료를받아왔던경우는전체고혈압환자의 45.4% 에불과하고, 당뇨병또한 77.5% 의환자들이기존에진단을받았으나, 32.5% 의환자들만이혈당을정기적으로조절해왔다고보고하였다. 따라서뇌경색증을예방하고관련합병증을최소화하기위해서는정상혈압을유지하고, 혈당을정기적으로체크하여조절하는것이중요하다고할수있으며, 뇌경색증환자의최선의기능회복을위한물리치료및작업치료시에도고혈압혹은당뇨가동반여부를고려하여대상자에게맞는적절한치료및운동이제공되어야할것이다. 뇌졸중발생시신체마비는가장많이동반되는증상으로 Kang 26 은경주시보건소에뇌병변장애로등록된대상자중뇌졸중과관련된 50세이상의대상자 651명을대상으로사전교육을받은보건소직원및맞춤형방문건강관리간호사들이미리작성된설문지를이용하여직접면접조사를실시한결과뇌졸중환자에게신체마비는우측혹은좌측, 양측신체마비로나타나고, 신체마비가있는경우중증장애 45.5%, 경증장애 53.5% 발생하였다고보고하였다. 본연구에서뇌경색증을진단받은전체대상자중편마비진단이동반된경우는 12.7% 인것으로나타났고, 연관규칙에서도신뢰도는 99.6% 로높게나타났지만지지도는 12.7% 로나타나선행연구와비교해대상자의신체적장애정도가낮았다. 이는뇌경색증환자를대상으로하였지만 Kang은뇌병변장애판정을받은뇌졸중환자를대상으로하여정도의차이는있지만모든대상자가장애를가진반면, 본연구의대상자들은장애판정유무에상관없이뇌경색증을진단받은경우만을대상으로하였고대상자의진단정보만을분석하였기때문에차이가생긴것으로사료된다. 그밖에위염은 6.1%, 위십이지장염은 11.3% 에서동반되었고, 위십이지장염은웹노드를이용한그래프와아프리오리모델링결과에서도뇌경색증과연관된질환으로나타났다. 이는 Kim 등 27 이뇌졸중을진단받고입원한 1,250명의환자를대상으로입원후 7일이내와퇴원당시담당전문의가작성한설문지를분석한결과뇌졸중환자의입원기간중합병증으로위염, 위궤양, 식도염과같은상부위장관합병증약 5% 의대상자에게나타난것과유사한결과로위장관질환은뇌경색증치료과정에서발생가능한합병증이라사료된다. 본연구에서뇌경색증과관련된질환은본태성고혈압, 편마비, 지단백질대사장애및기타지혈증, 인슐린 -비의존당뇨병, 위염및십이지장염등이있는것으로나타났고, 본태성고혈압은다른질환과비교해뇌경색증과연관성이가장높은질환이라할수있다. 또, 연관규칙중지단백질대사장애및 데이터마이닝을활용한뇌경색증과동반되는질환의연관성분석 79

6 J Kor Soc Phys Ther 2010;22(1):75-81 기타지혈증 (E78), 본태성고혈압 (I10) 뇌경색증(I63), 인슐린 - 비의존당뇨병 (E11), 본태성고혈압 (I10) 뇌경색증(I63) 과웹노드그래프를통해뇌경색증, 본태성고혈압, 인슐린 -비의존당뇨병, 지단백질대사장애및기타지혈증이서로연관성이있는질환임을확인할수있었다. 본연구결과는물리치료및작업치료등이필요한뇌경색증환자에게동반된여러질환을고려한물리치료를개발하는데필요한기초자료로활용될수있을것으로생각된다. 예를들어뇌경색증과동반되는당뇨병환자의경우미국당뇨병학회는주당 150분의중등도당뇨병예방프로그램의실행은유의한혈당저하효과가있다는연구 28 결과를기반으로당뇨병예방프로그램을포함한뇌경색증물리치료프로그램을구성할수있으며, 뇌경색증과고혈압이동반되는환자의경우는저항운동이유산소운동보다혈압하강효과가작은것으로보고 29 되고있기때문에저항운동보다유산소운동을포함한뇌경색증물리치료프로그램을구성할수있을것으로생각된다. 본연구는대용량의데이터를기반으로하였지만이는단일의료기관에내원한대상자만을대상으로하였다는한계점이있다. 또, Korea classification of disease (KCD)-4 코드를활용하였지만, 편마비로인한합병증중물리치료나작업치료의주대상이되는보행결손정도, 상하지기능부전정도및균형부전정도등을직접적으로연관시키지못했다는제한점이있다. 연구를바탕으로향후연구에서는더많은의료기관의뇌경색증환자를대상으로진단자료를수집하고보편화할수있는규칙을도출할것을제언한다. V. 결론본연구에서는뇌경색증과동반되는다빈도질환을파악하고, 질환들사이의연관성을데이터마이닝기법중연관규칙마이닝을이용해분석하였다. 이는데이터마이닝을대용량의임상자료분석에활용하여기존의분석방법과다른관점에서분석을시도한것으로질환사이의연관성을그래프로나타내고, 세가지이상의질환사이연관성을확인했다는데의의가있다. 본연구에서나타난결과는재활치료가필요한뇌경색증환자에게동반된여러질환을고려한물리치료를개발하는데필요한기초자료로활용될수있을것으로사료된다. Author Contributions Research design: Lee IH, Shin AM Acquisition of data: Lee IH, Shin AM Analysis and interpretation of data: Son CS, Lee IH, Shin AM Drafting of the manuscript: Son CS, Lee IH, Shin AM, Choi JH Administrative, technical, and material support: Park SY, Kim JH Research supervision: Park HJ, Kim YN Acknowledgements 본연구는산업자원부지방기술혁신사업 (RTI ) 지원으로수행되었음 참고문헌 1. Korea national statistical office. Annual report on the cause of death statistics. 2008, Retrieved August Lee IS. A case study on the group counseling for the stroke elderly. Korean Society of Welfare for the Aged. 2001;12: Mower DM. Brain attack. Treating acute ischemic CVA. Nursing. 1997;27(3): Yu KH, Bae HJ, Kwon SU et al. Analysis of 10,811 cases with acute ischemic stroke from Korean stroke registry: Hospital-based multicenter prospective registration study. J Korean Neurol Assoc. 2006;24(6): Lee MK, Kim JM, Kim WH, The effect of PNF leg patterns on activation of biceps and triceps in stroke patients. J Kor Soc Phys Ther. 2009;21(1): Siqueira Nete JI, Santos AC, Cabete Fabio SR et al. Cerebral infarction in patients aged 15 to 40 years. Stroke. 1996;27(11): Kristensen B, Malm J, Carlberg B et al. Epidemiology and etiology of ischemic stroke in young adults aged 18 to 44 years in northern Sweden. Stroke. 1997;28(9): Hardie K, Hankey GJ, Jamrozik K et al. Ten-year risk of first recurrent stroke and disability after first-ever stroke in the Perth Community Stroke Study. Stroke. 2004:35(3); Lee IH, Kim SH. Stroke Rehabilitation. Seoul, Epublic, 2004: Song JM, Kim SM. The effect of aquatic exercise on the improvement of physical and pulmonary function after stroke. J Kor Soc Phys Ther. 2009;21(2): Lee JH, Kwon YJ, Kim K. The effect of chest expansion and pulmonary function of stroke patients after breathing 80 Association Analysis of Comorbidity of Cerebral Infarction Using Data Mining

7 exercise. J Kor Soc Phys Ther. 2009;21(3): Chung SJ, Kim JS, Lee CH. Roles of risk factor changes, preceding infection, exposure to coldness, and psychologic stress. J Kor Neurol Ass. 1998;16(5): Yang JI, Rah UW, Moon HW et al. Studies of risk factors in stroke patients. J Korean Acad Rehab Med. 1997;21(4): Bae HS, Cho DH, Suk KH et al. Data mining using SAS Enterprise Miner. 2nd ed. Seoul, Kyowoosa, 2008: Tai YM, Chiu HW. Comorbidity study of ADHD: Applying association rule mining (ARM) to National Health Insurance Database of Taiwan. Int J M Inform. 2009;78(12):e Greenlund KJ, Giles WH, Keenan NL et al. Physician advice, patient actions, and health-related quality of life in secondary prevention of stroke through diet and exercise. Stroke. 2002;33(2): Kang HC, Han ST, Choi JH et al. Datamining with SAS Enterprise Miner 4.0; methodology and application. 3rd ed. Seoul, Jayuacademi, 2001: Huh J, Jeong KS, Huh SH et al. Clementine 7 manual. Seoul, Datasolution, 2003: Huh MH, Lee YG. Data mining modeling and case. 2nd ed. Seoul, Hannarae, 2008: Lee MS. Relating factors and health promoting behaviors of stroke patients. Pusan Catholic University. Dissertation of master s Degree Nam SW, Chung CS, Choi YC et al. Vascular risk factors related to stoke types in hypertensive subjects. J Korean Neurol Assoc. 1995;13(2): Bogousslavsky J, Castillo V, Kumral E et al. Stroke subtypes and hypertension. Primary hemorrhage vs infarction, large-vs small-artery disease. Arch Neurol. 1996;53(3): Lestro Henriques I, Bogousslavsky J, van Melle G. Predictors of stroke pattern in hypertensive patients. J Neurol Sci. 1996;144(1-2): Barrett-Connor E, Khaw KT. Diabetes mellitus: An independent risk factor for stroke? Am J Epidemiol. 1988;128(1): Lee YH. Differences of risk factors between hemorrhagic and ischemic strokes in hypertensive patients. Gyungsang National University. Dissertation of Master s Degree Kang MS. Associated factors with degree of disability of stroke patients in a rural community. Dongguk University. Dissertation of Master s Degree Kim KT, Kang MJ, Lee HJ et al. Clinical factors associated with functional status at discharge in stroke patients. J Korean Acad Rehab Med. 2003;27(3): American Diabetes Association. Standards of medical care in diabetes Diabetes Care. 2008;31(suppl 1):S Pescatello LS, Franklin R, Farard WB et al. American college of sports medicine position stand. Exercise and hypertension. Medicine and Science in Sports and Exercise. 2004;36(3): 데이터마이닝을활용한뇌경색증과동반되는질환의연관성분석 81

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