Cumulative Link Ordinal Outcome Neural Networks: An Evaluation of Current Methodology

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 analysis of several ordinal activation functions that utilize four key cumulative link functions used in ordinal regression: logit (logistic distribution), probit (normal distribution), complementary log log (cloglog), based on the Gompertz distribution, and log log (loglog), derived from the Gumbel distribution. The objective of this study was to systematically assess the performance of various activation functions in relation to the four link functions, focusing on how these functions affect the predictive accuracy of ordinal outcome neural networks. The findings indicate that the logit link consistently outperformed the other cumulative links across different datasets, achieving the highest percentage of correctly classified observations. The results of this study contribute to model selection considerations when analyzing complex health-related data, leading to more accurate predictions and an improved understanding of biomedical phenomena.