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 ...

An ordered logistic regression model suitable for analysing the severity of diabetes as an ordinal outcome was used. This study relies on secondary data, which do not capture detailed clinical ...

Ordinal regression and classification methods form a vital branch of statistical learning wherein the outcome variable possesses an inherent order. Unlike conventional classification problems ...

Abstract: Ordinal regression (OR), also called ordinal classification, is a special multi-classification designed for problems with ordered classes. Imbalanced data hinders the performance of ...

ABSTRACT: This study investigates retirement planning strategies and financial literacy among formal sector employees at Zambia’s Food Reserve Agency (FRA). Using a quantitative approach, the research ...

CORAL (COnsistent RAnk Logits) and CORN (Conditional Ordinal Regression for Neural networks) are methods for ordinal regression with deep neural networks, which address the rank inconsistency issue of ...

I modelled the early evolution of COVID-19 as a logistic function with 6 parameters: where σ(x,θ) represents the number of positive cases at x-th day after the 24th of February. I exploited BFGS ...