A Study on the Impact of Physical Inactivity on the Progression from General Chronic Diseases to Multiple Chronic Diseases: An Analysis Based on Association Rules analysis = 신체활동 부족이 일반 만성질환에서 다발성 만성질환으로의 진행에 미치는 영향: 연관 규칙 분석 (Association Rules analysis) 활용

국문 초록 (Abstract)

만성 질환의 발생과 유행은 인류의 생명 건강에 심각한 위협을 가하고 있다. 대부분의 만성 질환 간에 복잡한 연관성이 있기 때문에 많은 환자들이 동시에 두 가지 이상의 만성 질환을 앓고 있으며, 이 현상을 '다중 만성 질환'이라고 한다. 다중 만성 질환을 앓고 있는 개인은 더 나쁜 건강 결과를 직면할 뿐만 아니라 재입원률도 더 높으며, 만성 질환의 수가 증가함에 따라 의료 비용도 지속적으로 증가한다. 따라서 일반 만성 질환 환자가 다중 만성 질환 환자로 발전하는 것을 막는 것은 만성 질환 문제를 해결하는 핵심 중 하나이다. 신체 활동 부족은 대부분의 만성 질환의 주요 위험 요소이며, 운동은 만성 질환의 치료와 예방에 널리 사용되고 있다. 그러나 신체 활동 부족이 어떻게 일반 만성 질환 환자를 다중 만성 질환 환자로 발전시키는지에 대한 연구는 아직 불충분하며, 이는 가능한 다양한 만성 질환 조합 모델이 매우 복잡하기 때문이다. 연관 규칙 분석(Association Rules Analysis)은 데이터 마이닝의 중요한 분야로, 대규모 데이터베이스에서 항목 간의 중요한 연관성과 빈번한 패턴을 식별할 수 있다. 연관 규칙은 숨겨진 패턴을 광범위하게 탐색할 수 있는 강력한 데이터 마이닝 기법으로, 의료 데이터셋에서 특징 하위 집합을 포함한 예측 규칙을 식별하는 데 적합하다. 다른 연구 방법과 비교할 때, 연관 규칙 분석은 여러 이산 변수를 동시에 분석하고, 사전 정의된 기준에 따라 중요한 변수와 그 조합을 자동으로 필터링할 수 있다. 본 연구의 목표는 두 가지이다. 첫째, 데이터 시각화를 통해 한국의 만성 질환과 '신체 활동 부족'의 현재 상태를 이해하는 것이다. 둘째, 연관 규칙 분석을 사용하여 다양한 만성 질환 간의 복잡한 관계를 분석하고, 특히 '신체 활동 부족 (Physical Inactivity)'이라는 조건 하에서 다양한 만성 질환이 복잡한 관계 네트워크에서 어떤 역할을 하는지 밝혀내는 것이다. 이 두 가지 목표를 달성하기 위해 본 연구는 2016년부터 2021년까지의 한국 국민건강영양조사 데이터를 사용하였으며, 연구 대상은 46,828명이었다. 데이터 정제 및 필터링을 통해 최종적으로 32,860명의 데이터가 완전하고 요구 조건에 부합하는 연구 대상자가 선정되었다. 25가지 비전염성 만성 질환 및 관련 신체 활동 데이터를 사용하여 일반 통계 분석 및 데이터 마이닝을 수행하였다. 통계 분석 결과는 한국의 만성 질환 환자의 현황과 환자가 다중 만성 질환으로 전환되는 주요 연령대를 밝혔냈고, 연관 규칙 분석을 통해 총 56개의 연관 규칙을 발굴하였다. 결론적으로 폐경 후 여성의 만성 질환이 빠르게 증가하고 있으므로, 그들은 만성 질환의 예방과 치료에서 중점적으로 관리되어야 한다. 또한, 사회적 요인으로 인해 25세에서 30세 사이의 대다수 인구가 신체 활동 부족의 생활 방식을 채택하고 있으며, 이 문제를 완화할 수 있다면 만성 질환의 예방과 치료에 큰 도움이 될 것이다. 간경화, 만성 신장 질환, 암, 갑상선 질환 이 네 가지 만성 질환은 다른 만성 질환 또는 비활동적인 생활 방식과의 관련성이 약한 것으로 나타났다. 반면, 뇌졸중, 우울증, 심혈관 질환, 천식, 만성 안과 질환, 관절염, 골다공증 등의 만성 질환은 비활동적인 생활 방식 및 다른 만성 질환의 영향을 받아 발병률이 현저하게 증가하는 것으로 나타났다. 기초 발병률이 높은 대사성 질환, 예를 들어 비만, 고혈압, 고지혈증 등의 환자는 신체 활동 부족의 생활 습관이 있을 경우 다른 만성 질환에 걸릴 가능성이 크게 증가하는 것으로 나타났다.

목차 (Table of Contents)

• I. Introduction 7
• 1. Rationale for the Study 7
• 2. Research Objective 14
• II. Background. 15
• 1. Chronic Diseases and MCD. 15
• 2. Physical Inactivity and Chronic diseases. 23
• 3. Association Rule analysis in related fields. 28
• III. Methods. 32
• 1. Research Subjects 32
• 2. Research Variables. 34
• 1) Chronic Diseases. 34
• 2) Physical Inactivity . 35
• 3. Analysis Methods. 36
• 1) Statistical analyses and data visualization. 36
• 2) Association Rules analyses 37
• 3) Apriori Algorithm 39
• IV. Results. 41
• 1. Mathematical Statistics Results 41
• 1) Prevalence of Chronic Diseases. 41
• 2) Overall Trends and Gender Differences in MCD 43
• 3) Current Status of Patients with Major Chronic Diseases 46
• 4) Physical Inactivity in South Korea 54
• 2. Results of Association Rules analysis 57
• 1) Physical Inactivity and Stroke 58
• 2) Physical Inactivity and Depression 59
• 3) Physical Inactivity and Cardiovascular diseases. 60
• 4) Physical Inactivity and Asthma 62
• 5) Physical Inactivity and Chronic eye disease. 63
• 6) Physical Inactivity and Arthritis. 65
• 7) Physical Inactivity and Osteoporosis. 66
• V. Discussion. 68
• 1. Current Status of Chronic Diseases and Physical Inactivity in South Korea 68
• 1) Gender Differences in Chronic Diseases 68
• 2) Prevalence of Major Chronic Diseases 72
• 3) Trend of Single Chronic Disease Patients Developing MCD 80
• 4) Current Status of Physical Inactivity 83
• 2. Exploring Relationships Between Chronic Diseases 87
• 1) Overall Results of Association Rules analysis. 87
• 2) Results of Major Chronic Diseases in Association Rules analysis 90
• Limitations 96
• VI. Conclusions 97
• References. 100

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