인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Diabetes is a lifelong condition that occurs when the pancreas loses its ability to secrete insulin or experiences a significant reduction in insulin production. Early identification of high-risk patients is crucial for timely interventions and improved outcomes. Traditional clinical risk prediction models rely on regression analysis using clinical, sociodemographic, and anthropometric data; however, they have limitations in terms of accuracy and generalizability. This research proposes a diagnostic strategy leveraging machine learning (ML) techniques, specifically the XGBoost algorithm optimised with Optuna, to enhance high-risk prediction based on laboratory parameters. The study utilises an open-access diabetes dataset incorporating patient demographics, laboratory test results, and clinical outcomes. Data preprocessing, including cleaning, normalisation, and feature extraction, is performed using an Adaptive Tree-Structured Parzen Estimator (ATPE) and XGBoost model. The proposed model outperforms conventional classification models, achieving 83% accuracy, 80% precision, 78% recall, and a 78% F1 score. A comprehensive correlation and confusion matrix evaluation highlights the model's effectiveness in distinguishing high-risk patients. Findings indicate that integrating machine learning (ML)-based risk classification frameworks with laboratory test-based diagnostic strategies improves predictive accuracy and patient stratification. However, data quality, population diversity, and real-time applicability remain challenges. Future research should explore the integration of real-time data from wearable devices and expand model deployment to other chronic and rare diseases, enhancing adaptability and clinical decision-making.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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