Machine Learning (PIERS-ML) model and the logistic regression-based fullPIERS model, consecutive performance deteriorates over time.

This study addresses this challenge by leveraging machine learning (ML) models and explainable artificial intelligence (XAI) techniques to predict the stability ... (GBoost), k-Nearest Neighbors (k-NN ...

This project builds a Logistic Regression Model using Scikit-Learn to classify flowers in the Iris dataset. The trained model is saved using Joblib for future predictions. Libraries Used joblib: Saves ...

Here, a coronary artery disease prediction system is developed using logistic regression algorithm. For the experimentation purpose, heart disease datasets present in UCI repository are used. The ...

Adjusted predictions or marginal means are often easier to understand than raw regression coefficients ... however, there is also a plot()-method to easily create publication-ready figures. Adjusted ...

Methods This prediction model used Ethiopian national TB prevalence survey data and included 5459 presumptive TB cases from all regions of Ethiopia. Logistic regression was used to determine which ...

When developing a clinical prediction rule that is based ... where the weights are the regression coefficients from the multiple regression model (log odds ratios (ORs) for logistic models and log HRs ...

Motivation: There is an ongoing search for definitive and reliable biomarkers to forecast or predict imminent seizure onset ... who underwent intracranial EEG monitoring. We used LASSO logistic ...