Logistic regression is a statistical method used to model binary outcome variables, such as whether a patient recovers or not, using a set of predictors. There are many competing methods for ...

Abstract: Fuzzy classification models are important for handling uncertainty and heterogeneity in high-dimensional data. Although recent fuzzy logistic regression approaches have demonstrated ...

What if you could create your very own personal AI assistant—one that could research, analyze, and even interact with tools—all from scratch? It might sound like a task reserved for seasoned ...

Abstract: Logistic regression is a fundamental and widely used statistical method for modeling binary outcomes based on covariates. However, the presence of missing data, particularly in settings ...

This set of notebooks enables the analysis of comorbidities associated with male infertility using structured EHR data. First, we identified nonoverlapping patients with male infertility and patients ...

Logistic regression is a tool used in data science and machine learning to predict binary outcomes. Applications range from determining customer behaviors to diagnosing diseases. While the concept of ...

ABSTRACT: Over the past ten years, there has been an increase in cardiovascular disease, one of the most dangerous types of disease. However, cardiovascular detection is a technique that analyzes data ...

ABSTRACT: With the rapid growth of e-commerce and online transactions, e-commerce platforms face a critical challenge: predicting consumer behavior after purchase. This study aimed to forecast such ...

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...