Emerging environmental contaminants (EECs) such as pharmaceuticals, pesticides, and industrial chemicals pose significant challenges for detection and identification due to their structural diversity and lack of analytical standards. Traditional targ...
The Mahananda River basin, located in Eastern India, faces escalating flood risks due to its complex hydrology and geomorphology, threatening socioeconomic and environmental stability. This study presents a novel approach to flood susceptibility (FS)...
This study tackles the challenge of accurately estimating pasture biomass by integrating proximal sensing, remote sensing, and machine learning techniques. Field measurements of vegetation height collected using the PaddockTrac ultrasonic sensor were...
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).
BACKGROUND: Severe pneumonia has a poor prognosis and high mortality. Current severity scores such as Acute Physiology and Chronic Health Evaluation (APACHE-II) and Sequential Organ Failure Assessment (SOFA), have limited ability to help clinicians i...
OBJECTIVES: Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) present similar symptoms in the early stage, complicating their differentiation. This study aims to develop a classification model using radiomic features from MRI T2-w...
Adipogenesis is intricately linked to the onset and progression of muscle aging; however, the relevant biomarkers remain unclear. This study sought to identify key genes associated with adipogenesis in the context of muscle aging. Firstly, gene expre...
BACKGROUND: Depression is a common complication after a stroke that may lead to increased disability and decreased quality of life. The objective of this study was to develop and validate an interpretable predictive model to assess the risk of depres...
Cardiovascular disease is the leading cause of mortality globally, necessitating precise and prompt predictive instruments to enhance patient outcomes. In recent years, machine learning methodologies have demonstrated significant potential in enhanci...
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