Risk prediction has been used in the primary prevention of cardiovascular disease for >3 decades. Contemporary cardiovascular risk assessment relies on multivariable models, which integrate ...

QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...

In a recent study published in the journal Scientific Reports, researchers developed a pattern neural network (PNN) model that combined a novel measure of total antioxidant status with traditional ...

ABSTRACT: Heart disease remains one of the leading causes of mortality worldwide, accounting for millions of deaths annually. Early detection of individuals at risk is essential for reducing ...

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Abstract: The abstract is an imperfect defect detection model meant to classify various defects of castings. It presents an excellent precision, recall, and $\mathbf{F 1}$-score of six classes of ...

ABSTRACT: This paper aims to investigate the effectiveness of logistic regression and discriminant analysis in predicting diabetes in patients using a diabetes dataset. Additionally, the paper ...

Abstract: E-health sensors and wearables play an important role in the detection and classification of many chronic diseases. A chronic disease requires active monitoring and its severity increases ...

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 ...

Background: Early identification of Type 1 Diabetes Mellitus (T1DM) in pediatric populations is crucial for implementing timely interventions and improving long-term outcomes. Peripheral blood ...