The diagnosis of asymptomatic tuberculosis (TB) remains challenging due to an early disease stage. This study aimed to identify and validate plasma biomarkers for asymptomatic TB by integrating the Olink proteomics with multiple machine learning algo...
Training AI models on imbalanced datasets with skewed class distributions poses a significant challenge, as it leads to model bias towards the majority class while neglecting the minority class. Various methods, such as Synthetic Minority Over Sampli...
BACKGROUND: Lung cancer is a leading cause of cancer-related mortality worldwide. Accurate staging of mediastinal lymph nodes is a crucial step in determining appropriate treatment approaches. Current noninvasive diagnostic methods do not provide suf...
BACKGROUND: The preoperative identification of (isocitrate dehydrogenase) IDH-mutant low-grade gliomas (LGGs) is critical for personalized treatment planning. We aimed to develop a streamlined machine-learning model using key clinical features for ra...
This study aims to develop accurate and efficient machine learning models to predict the concentrations of phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) in 10 legume species naturally growing in the Çamlıhemşin district of Rize prov...
Depression is a significant public health issue, consistently ranking among the leading causes of mortality, reduced quality of life, and economic burden. Despite available treatments, approximately one-third of patients exhibit resistance to standar...
The mandible is one of the most reliable in sex determination in forensic anthropology. The shape of the mandible provides valuable information regarding the male and female distinctions. Machine learning algorithms are widely used for various applic...
Creative experiences may enhance brain health, yet metrics and mechanisms remain elusive. We characterized brain health using brain clocks, which capture deviations from chronological age (i.e., accelerated or delayed brain aging). We combined M/EEG ...
Modern enterprises grapple with complex financial data and multidimensional risk interdependencies in their operations. Machine learning offers transformative potential for tax risk assessment and smart auditing solutions. This research analyzes 3,23...
This study investigates the use of machine learning (ML) algorithms to support faster and more accurate diagnosis of polycystic ovary syndrome (PCOS), with a focus on both predictive performance and clinical applicability. Multiple algorithms were ev...
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