For the Preeclampsia Integrated Estimate of RiSk (PIERS) Machine Learning (PIERS-ML) model and the logistic regress ...

While ML models are powerful tools for predicting diabetes, their lack of interpretability presents a major challenge for clinical adoption. Healthcare professionals require AI models to not only be ...

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 ...

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 ...

Logistic regression analyses were used to identify the socio-demographic and clinical features associated with cancer up to 2 years before diagnosis. A risk prediction model was ... or ex smokers ...

Finally, the Stacking Classifier technology was harnessed to produce ACPs predictions. We incorporated the SHAP (Shapley Additive explanation) model (Lundberg and Lee, 2017) to rank the influential ...

Thus, this study takes an artificial intelligence approach, whereby clinical data-driven machine learning was utilized to predict the in-hospital mortality ... The machine learning algorithms included ...