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
After childbirth, women experience significant psychological, physiological, and hormonal changes. To better diagnose individuals at risk of postpartum complications, predictive models utilizing data mining and machine learning techniques can be instrumental. The C4.5 decision tree algorithm effectively analyzes multiple variables to identify key relationships. The objective of the study was to predict Female Sexual Interest/Arousal Disorder (FSIAD) six months postpartum using serum adiponectin levels and biopsychosocial factors through decision tree analysis. A longitudinal cohort study was conducted with data from 170 pregnant women, collecting data at three points: the third trimester, 40 days postpartum, and six months postpartum. Blood samples were analyzed for adiponectin, estradiol, and testosterone. At the same time, participants completed assessments using the Female Sexual Function Index (FSFI), the World Health Organization Well-Being Index, a socioeconomic index, and a questionnaire on non-biological factors affecting sexual desire. The prevalence of FSIAD was found to be 29.7%, and the model achieved 93.7% accuracy in predicting FSIAD. Significant predictors included serum adiponectin (T1), estrogen (T3), waist circumference (T2, T3), orgasm disorder, and pain disorder, all with p-values < 0.05. The model provides a clinically valuable tool for early identification of at-risk women, allowing for timely intervention and personalized postpartum care.
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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