Background The Chinese government initiated a compulsory services programme (CSP) to provide skilled general practitioners ...
Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...
Objectives Curable sexually transmitted infections (STIs) heavily rely on laboratory testing methods. Unfortunately, these diagnostic tools are infrequently used in certain regions of the country, ...
Objectives To identify caregiver burden profiles among informal caregivers of children and adolescents with type 1 diabetes and explore associated factors. Design A multicentre cross-sectional survey ...
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 ...
Background Cancer survivors have an increased risk of heart failure, but this is balanced by the risk of death from other ...
Purpose: To develop a machine learning model to predict anatomical response to anti-VEGF therapy in patients with diabetic macular edema (DME). Methods: This retrospective study included patients with ...
Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...
Struggling to understand how logistic regression works with gradient descent? This video breaks down the full mathematical derivation step-by-step, so you can truly grasp this core machine learning ...
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