ProjectPage/index.html at root · Choiyejin412/ProjectPage · GitHub

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<!DOCTYPE html>
<html>
<head>
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  <meta property="og:title" content="SOCIAL MEDIA TITLE TAG"/>
  <meta property="og:description" content="SOCIAL MEDIA DESCRIPTION TAG TAG"/>
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  <title>Mahcine Learning-based Hypertension Prediction</title>
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  <section class="hero">
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            <h1 class="title is-1 publication-title">Medical Learning-based Hypertension Prediction</h1>
            <div class="is-size-5 publication-authors">
              <!-- Paper authors -->
              <span class="author-block">
                    Yeajin Choi
                  <sup>1</sup></span>
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                  <div class="is-size-5 publication-authors">
                    <span class="author-block"><sup>1</sup>Division of Digital Healthcare, Yonsei University</span>
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        <h2 class="title is-3">Introduction</h2>
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            고혈압은 전 세계적으로 중요한 건강 문제 중 하나로, 조기 발견과 관리가 필수적인 질병입니다. <br>
            고혈압은 심혈관 질환, 뇌졸중, 신부전 등 심각한 합병증을 유발할 수 있으므로, 예방과 관리를 위해 정기적인 건강검진이 중요하게 여겨집니다..<br>
            그렇기에 건강검진 데이터는 개인의 건강 상태를 종합적으로 파악할 수 있는 중요한 자료로, 이를 활용하여 고혈압을 조기에 예측하고 적절한 조치를 취할 수 있습니다. <br>
            본 연구에서는 건강검진 데이터를 활용하여 고혈압을 예측함으로써, 개인의 건강 관리에 기여하고자 합니다.
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          <h4>1.   데이터 수집 및 전처리</h4>
          <ul>
              <li>공공 건강검진 데이터를 수집하여 사용하였습니다.</li>
              <li>결측값을 처리하고, 범주형 변수를 수치형으로 변환하는 등의 데이터 전처리 과정을 수행하였습니다.</li>

          </ul>
          <h4>2.   모델 선택 및 학습</h4>
          <ul>
              <li>고혈압 예측을 위해 RandomForestRegressor 모델을 선택하였습니다.</li>
              <li>데이터를 학습 데이터와 테스트 데이터로 분할하여 모델을 학습시켰습니다.</li>
          </ul>
          <h4>3.   예측 및 평가</h4>
          <ol>
              <li>학습된 모델을 사용하여 테스트 데이터에 대한 고혈압을 예측하였습니다.</li>
              <li>모델의 성능을 Mean Squared Error (MSE)와 R^2 Score를 통해 평가하였습니다.</li>
          </ol>
          <h4>4.   결과 요약</h4>
            <ol>
              <li>모델의 예측 결과를 시각화하여 실제 값과 예측 값을 비교하였습니다.</li>
              <li>예측 결과에 대한 분석과 함께 모델의 성능을 평가하였습니다.</li>
              <li>고혈압 예측을 위해 RandomForestRegressor 모델을 선택하였습니다.</li>
            </ol>
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        <h2 class="title is-3">Results</h2>
        <img class="content has-text-justified">
        <ul>
          <li>
            데이터 전처리 과정
          </li>
        </ul>
        <ol>
          <li>결측값 처리 방법</li>
          <ul>
            <li>데이터셋을 로드한 후 결측값을 확인하고, 평균값으로 대체하였습니다.</li>
            <li>결측값이 있는 열을 삭제하는 대신, 평균값으로 채뤄 데이터 손실을 최소화했습니다.</li>
            <img src="static/images/데이터 전처리.png" alt="MY ALT TEXT"/>
            <img src="static/images/범주형 변수를 수치형으로.png" alt="MY ALT TEXT"/>
            <img src="static/images/데이터 분할.png" alt="MY ALT TEXT"/>
          </ul>

          <li>범주형 변수 변환 방법</li>
          <ul>
            <li>범주형 변수인 성별, 연령대코드, 시도코드 등의 데이터를 One-Hot Encoding 을 사용하여 수치형으로 변환하였습니다.</li>
            <li>이를 통해 범주형 변수를 모델이 이해할 수 있도록 처리했습니다.</li>
            <img src="static/images/모델 학습.png" alt="MY ALT TEXT"/>
            <img src="static/images/예측,평가.png" alt="MY ALT TEXT"/>
          </ul>

          <li>데이터 분할 (훈련 데이터와 테스트 데이터)</li>
          <ul>
            <li>전체 데이터를 학습 데이터와 테스트 데이터로 분할하여 모델의 학습과 평가에 사용하였습니다.</li>
            <li>학습 데이터와 테스트 데이터의 비율은 80:20으로 설정하였습니다.</li>
            <img src="static/images/시각화.png" alt="MY ALT TEXT"/>
            <img src="static/images/중요 변수 시각.png" alt="MY ALT TEXT"/>
          </ul>

        </ol>


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            Description.
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      <pre><code>@inproceedings{
    lee2024entropy,
    title={Entropy is not Enough for Test-time Adaptation: From the Perspective of Disentangled Factors},
    author={Jonghyun Lee and Dahuin Jung and Saehyung Lee and Junsung Park and Juhyeon Shin and Uiwon Hwang and Sungroh Yoon},
    booktitle={The Twelfth International Conference on Learning Representations},
    year={2024},
    url={https://openreview.net/forum?id=9w3iw8wDuE}
}</code></pre>
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        <h2 class="title is-3">Reference</h2>
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          <p>
            [1] 용왕식, 박일수, 강성홍, 김원중, 김공현, 김광기, 박노례. (2006). 고혈압 발생 예측 모형 개발. 보건교육건강증진학회지, 23(4), 13-28. <br>
            [2] 이종원, 정인철, 김갑열, 조진수. (2021). 신경망 알고리즘을 활용한 고혈압 수치 예측 모델 연구. 2021 한국차세대컴퓨팅학회 춘계학술대회, 356-358.
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