BACKGROUND: Fatty liver disease is a common condition linked to metabolic syndrome, cardiovascular diseases, and liver cirrhosis, and timely, accurate diagnosis is crucial. In clinical studies, incorporating deep learning models often faces the chall...
BACKGROUND: The multifactorial mechanisms driving childhood obesity, a global public health challenge, are yet to be fully elucidated. We aimed to develop and externally validate three widely applied machine learning models alongside logistic regress...
UNLABELLED: Early identification of children at high risk for moderate-to-severe obstructive sleep apnea (OSA) is crucial for timely intervention, yet is often hindered by limited access to polysomnography (PSG). We aimed to develop an interpretable ...
BACKGROUND: Preoperative prediction of surgical difficulty in robotic-assisted total mesorectal excision for rectal cancer remains challenging. While pelvic anatomical parameters measured by MRI have been associated with surgical complexity in laparo...
Anais da Academia Brasileira de Ciencias
Dec 19, 2025
This research aims to propose a machine learning approach to classify dropout outcomes among students in Statistics undergraduate programs in Brazil, identifying the most important factors associated with this phenomenon. This study uses microdata fr...
Athletes face a higher risk of mental health disorders compared to the general population, and prior theoretical and empirical work suggests that personality traits and training-related factors may play important roles in shaping athletes' mental hea...
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Dec 16, 2025
PURPOSE: This study aimed to establish and validate a machine learning model for predicting moderate-to-severe cancer-related fatigue (CRF) 2 years after completion of anti-tumor therapy in breast cancer patients.
BACKGROUND AND OBJECTIVES: Women's empowerment is a vital issue in lower-middle-income developing countries like Bangladesh, where it plays a pivotal role in advancing development across the nation. Thus, this study aimed to identify the influential ...
OBJECTIVES: To develop a machine learning (ML)-based predictive model to determine the key predictors of dissatisfaction after occupational injury (OI).
BACKGROUND: Early identification of students at academic risk is critical in health sciences education, particularly in regions prioritizing healthcare workforce development. This study evaluated the application of established machine learning (ML) c...
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