In a study titled Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples, published in the ...

New TMS biomarkers combined with machine learning accurately classified major depressive disorder. Learn more about this ...

Background Patients with severe aortic stenosis (AS) are at high risk of mortality, regardless of symptom status. Despite ...

Objective: To evaluate and to compare machine learning models for predicting hypertension in patients with diabetes using routine clinical variables. Methods: Using Behavioral Risk Factor Surveillance ...

APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering ...

ABSTRACT: Smart health refers to the integration of cutting-edge technologies into healthcare systems to improve patient care and apply intelligent clinical decision-making. The study investigates how ...

Chances are, you’ve seen clicks to your website from organic search results decline since about May 2024—when AI Overviews launched. Large language model optimization (LLMO), a set of tactics for ...

ABSTRACT: Atrial fibrillation (AF) is a leading cardiac arrhythmia associated with elevated mortality risk, particularly in low-resource settings where early risk stratification remains challenging.

Introduction: Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as ...

Classification of gas wells is an important part of optimizing development strategies and increasing the recovery. The original classification standard of gas wells in the Sulige gas field has weak ...