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Background Lupus nephritis (LN) is a leading cause of mortality in patients with systemic lupus erythematosus (SLE), and the accurate classification of renal pathological subtypes is crucial for reducing mortality rates ...

BackgroundThe dengue virus (DENV) can cause various clinical syndromes and organ damage, known as dengue fever, with the probability of developing severe dengue. However, the underlying mechanisms of host response agains...

Abstract In many real-world applications, binary and multi-class classification problems involving imbalanced data and overlapping boundaries present a significant challenge for traditional machine learning algorithms. I...

Medical image prediction plays a very significant role in clinical decision-making and early detection and diagnosis of different diseases. However, the quality of medical images has a huge impact on the predictive model...

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing mach...

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors ...

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are t...

Data mining is the discovery of patterns and regularities from large amounts of data using machine learning algorithms. This can be applied to object recognition using image processing techniques. In fruits and vegetabl...

Di-Higgs production at the LHC associated with missing transverse energy is explored in the context of simplified models that generically parameterize a large class of models with heavy scalars and dark matter candidates...

BackgroundOpioid overdose remains a leading cause of preventable death in the United States. Existing approaches to identify individuals at elevated risk rely on imprecise rule-based criteria that misclassify patients’...

Abstract This research employs hybrid machine learning techniques to optimize the mechanical properties of 3D-printed polylactic acid (PLA) lattice structures. By integrating random forest (RF) and XGBoost (XGB) algorith...

Abstract Background Central nervous system (CNS) infections in cattle are a major cause of economic loss and mortality. Machine learning (ML) techniques are gaining widespread application in solving predictive tasks in b...

IntroductionOsteoarthritis (OA) is a common joint disease characterized by progressive biomechanical deterioration of cartilage tissue. This study aims to develop an explainable ensemble machine learning model capable of...

BackgroundGlobal population aging is accelerating, and China faces a significant challenge with “aging before affluent.” The disabled older adults population in China is projected to reach 58 million by 2050. Early i...

Football is the world’s most popular sport, and evaluating the market value of players is crucial for clubs and managers in making informed decisions regarding transfers, contracts, and financial planning. This st...

Abstract Background Interstitial lung disease (ILD) is characterized by marked heterogeneity and an overall poor prognosis, with many patients experiencing rapid progression and high short-term mortality. Accurate early ...

BackgroundInterstitial fibrosis and tubular atrophy (IFTA) are key pathological features of chronic kidney damage and progression in diabetic nephropathy (DN). Early identification of patients at higher risk of IFTA may ...

ObjectiveDespite the widespread use of immune checkpoint inhibitors (ICIs) improving survival outcomes in non-small cell lung cancer (NSCLC) patients, immune-related adverse events (irAEs) triggered by ICIs have become a...

Financial institutions worldwide face significant challenges in detecting and preventing illicit financial activities, such as money laundering and terrorism financing. Traditional rule-based methods often generate high ...

Accidental ignition of energetic compounds by electrostatic discharge poses significant safety risks, highlighting the need for precise prediction of electric spark sensitivity. Here we develop a novel interpretable mach...

Abstract Background Accurate prediction of response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) is essential for optimizing treatment decisions. This study aimed to deve...

Abstract Background Chronic conditions cause millions of deaths annually worldwide. Remote patient monitoring using wearable devices and sensors, combined with machine learning (ML), offers promising strategies for disea...

BackgroundThe integration of artificial intelligence (AI) into emergency medicine holds promise for enhancing early diagnosis and clinical decision-making. Traditional diagnostic approaches often rely on physician judgme...

Abstract Background Understanding how the respiratory microbiota matures with age is key to improving poultry health and pathogen surveillance, yet the ecological processes shaping this transition remain elusive. We aime...