Objective To determine pregnant women and new mothers’ perception of risks in pregnancy ... outcome is treated as missing. We report the number of individuals included in each analysis (n) along with ...

Abstract: Incremental Attribute Learning (IAL) gradually trains features in one or more size, which can be used to solve regression problems. Previous studies showed that feature ordering is crucial ...

ABSTRACT: Ordinal outcome neural networks represent an innovative and ... functions that utilize four key cumulative link functions used in ordinal regression: logit (logistic distribution), probit ...

The objective of this paper is to use machine learning models to predict number of women above 35 with pregestational diabetes having high risk of abnormal pregnancy ... Forest, Logistic Regression ...

Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely ...

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 ...

Learn what is Logistic Regression Cost Function in Machine Learning and the interpretation behind it. Logistic Regression Cost function is "error" representation of the model. It shows how the ...

Objectives To describe the outcomes of pregnancies in antiphospholipid antibody (aPL)-positive patients since the inception of the AntiPhospholipid Syndrome Alliance for Clinical Trials and ...