Objective To assess the magnitude and associated factors of suicidal behaviour and non-suicidal self-injury (NSSI) among ...

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...

Background Diffuse gastric cancer (DGC) is the most common manifestation in germline CTNNA1 variant carriers, with one study estimating a 49–57% lifetime risk by age 80. Knowledge on CTNNA1-associated ...

Introduction: The olive tree (Olea europaea L.) has cultural, economic, and environmental importance in the Mediterranean region. In the last two decades, olive cultivation has shifted from ...

This project uses concepts from the TV show The Good Place to explore binary and multinomial logistic regression. The dataset contains behavioral features from 1,000 individuals—such as how often they ...

ABSTRACT: Ordinal outcome neural networks represent an innovative and robust methodology for analyzing high-dimensional health data characterized by ordinal outcomes. This study offers a comparative ...

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 model ...

Industrial organizations are racing to implement AI, yet many struggle to demonstrate concrete value from their investments. The missing element isn't better algorithms or more data; it's clarity ...

ABSTRACT: Road traffic accidents are one of the global safety and socioeconomic challenges. According to WHO (2024), it has caused over 1.19 million annual fatalities. It is also projected to cause ...