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
Abstract Background Each year, approximately 2.5 million newborns die globally, with developing countries bearing the impact of this crisis. Sub-Saharan Africa has the highest neonatal mortality rate, with Ethiopia facing alarmingly high figures, particularly in rural areas where mortality is significantly higher due to poor healthcare access and socio-economic challenges. Methods This study aimed to develop a predictive model for neonatal mortality in rural Ethiopia using secondary data from the Ethiopian Demographic and Health Surveys (2000–2019). The dataset included 29,048 instances and 22 relevant features, which were preprocessed to handle missing values and balance the class distribution using the Synthetic Minority oversampling technique. Several ensemble machine-learning algorithms, including Random Forest, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and CatBoost, were applied to build the model. Additionally, the logistic regression algorithm was employed to enhance transparency and interpretability and for comparative analysis. Model performance was evaluated based on accuracy, precision, recall, F1 score, and Receiver Operating Characteristic—Area Under the Curve. Results Among the algorithms tested, categorical boosting achieved the highest performance with 97.5% accuracy, 97.52% precision, 97.5% recall, 97.5% F1 score, and an exceptional Receiver Operating Characteristic—Area Under the Curve value of 99.57%. Key risk factors identified include BCG vaccination status, the number of under-five children in the household, recent diarrhea episodes, and iron tablet intake during pregnancy. Valuable feedbacks from community health workers were provided on these factors, helping to refine their impact on neonatal mortality. Conclusions This study developed an effective predictive model for neonatal mortality in rural Ethiopia, providing actionable insights for targeted interventions. The model underscores the importance of improving healthcare access, maternal health, and policy reforms, with the potential to reduce neonatal mortality through mobile health apps and policymaker collaboration.
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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