BACKGROUND: Osteoporosis has become a significant public health concern that necessitates the application of appropriate techniques to calculate disease risk. Traditional methods, such as logistic regression,have been widely used to identify risk fac...
International journal of medical informatics
40157246
BACKGROUND: Logistic regression (LR) has traditionally been the standard method used for predicting binary health outcomes; however, machine learning (ML) methods are increasingly popular.
International journal of medical informatics
40147417
OBJECTIVE: To compare the performance of machine learning and logistic regression algorithms in predicting emergence delirium (ED) in elderly patients.
: Glaucoma (GL) classification is crucial for early diagnosis and treatment, yet relying solely on stand-alone models or International Classification of Diseases (ICD) codes is insufficient due to limited predictive power and inconsistencies in clini...
: This study aims to evaluate the predictive value of comprehensive data obtained in obstetric clinics for the detection of stillbirth and the predictive ability set of machine learning models for stillbirth. : The study retrospectively included all ...
Preterm birth (PTB), defined as delivery before 37 weeks, affects 15 million infants annually, accounting for 11% of live births and over 35% of neonatal deaths. While advanced maternal age (≥ 35 years) is a known risk factor, PTB risk in women under...
BACKGROUND: Colorectal polyps are precancerous diseases of colorectal cancer. Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer. Endoscopic mucosal resection (EMR) is a common polypectomy pro...
OBJECTIVES: This study aimed to employ machine learning algorithms to predict the factors contributing to zero-dose children in Tanzania, using the most recent nationally representative data.
OBJECTIVES: This study aimed to compare the performance of five machine learning algorithms to predict diabetes mellitus based on lifestyle factors (diet and physical activity).
BACKGROUNDS: Gastric cancer (GC) is a prevalent malignancy affecting the digestive system. We aimed to develop a risk prediction model based on endoscopic atrophy classification for GC.