Objective SLE is a heterogeneous systemic autoimmune disease with diverse clinical manifestations. We aimed to identify ...

Background Systolic murmurs are common in adults without significant valvular heart disease (VHD) and are more prevalent in ...

Against the backdrop of accelerated glacier melting caused by global warming, dynamic monitoring of glacial lake water quality has become an important topic in environmental science. There is a ...

Abstract: Logistic regression is a supervised binary classification algorithm in machine learning. It is an important part of neural network and convolutional neural network. An important part of the ...

ABSTRACT: Automatic detection of cognitive distortions from short written text could support large-scale mental-health screening and digital cognitive-behavioural therapy (CBT). Many recent approaches ...

Machine learning algorithms are widely used for disease prediction, but the choice of sampling method is a factor that affects model performance, particularly in large populations, limitations on the ...

Stroke is one of the leading causes of death and disability worldwide, making early screening and risk prediction crucial. Traditional methods have limitations in handling nonlinear relationships ...

Abstract: This study addresses the lack of comprehensive evaluations of feature scaling by systematically assessing 12 techniques, including less common methods such as VAST and Pareto, in 14 machine ...

Amidst the rising global interest in Wushu, identifying core factors in martial arts techniques is vital for offering scientific and unbiased training, enhancing practitioners’ skills. This study ...

Logistic Regression is one of the most widely used algorithms in machine learning, especially when it comes to solving classification problems. Yet, its name often confuses beginners: why is it called ...

Customer retention is a critical challenge for telecom companies, and understanding customer churn can significantly improve business strategies. This paper focuses on developing an accurate ...

As biomarker studies employ increasingly complex and expensive genomics and other correlative methods, it is increasingly important to rigorously design these studies and analyze the downstream ...