Devdiscourse

Stacked AI model improves credit default forecasting

A new study suggests that lenders may get their strongest overall read on credit default risk by combining several machine ...
RMD Open

Distinct pattern of comorbidities hinders treatment target attainment in SLE through persistent disease activity and delayed glucocorticoid tapering: longitudinal data fro…

Background Remission and low-disease activity are recommended targets in systemic lupus erythematosus (SLE), yet many ...
IEEE

Predictive Modeling of Scalability and Mobility in Cloud Architectures Using Logistic Regression

Abstract: - this research paper explores the possibilities of using the Logistic Regression Framework as means of controlling the scalability and mobility of cloud computing servers. The aim of this ...
IEEE

Machine Learning–Based Hypertension Risk Stratification Using Logistic Regression and XGBoost

Abstract: Hypertension is a critical global health concern, necessitating accurate prediction models and effective prescription decisions to mitigate its risks. This study proposes a hybrid machine ...
Frontiers

A clinical prediction model for delirium tremens: development and validation in alcohol-dependent patients using multivariable logistic regression

Delirium tremens (DT) is a severe complication of alcohol withdrawal. This study aimed to develop and validate a prediction model for DT risk in hospitalized patients with alcohol dependence, using ...
MediaPost

How Marketing Mix Modeling Is Becoming A Must-Have Predictive Tool

The techniques that have served marketers for over fifty years are evolving rapidly, driven by artificial intelligence, increasing market volatility and a fundamental shift in what we expect ...