A new recurrent neural network based predictive model for Faecal Calprotectin analysis: A retrospective study

TL;DR

This study develops a novel Echo State Network-based predictive model for Faecal Calprotectin analysis, significantly outperforming traditional logistic regression in diagnosing intestinal inflammation related to IBD.

Contribution

It introduces a new recurrent neural network architecture specifically designed for FC analysis, enhancing predictive accuracy over existing methods.

Findings

ESN-based model outperforms logistic regression

Statistically significant improvement in predictive accuracy

Demonstrates potential for clinical application in IBD diagnosis

Abstract

Faecal Calprotectin (FC) is a surrogate marker for intestinal inflammation, termed Inflammatory Bowel Disease (IBD), but not for cancer. In this retrospective study of 804 patients, an enhanced benchmark predictive model for analyzing FC is developed, based on a novel state-of-the-art Echo State Network (ESN), an advanced dynamic recurrent neural network which implements a biologically plausible architecture, and a supervised learning mechanism. The proposed machine learning driven predictive model is benchmarked against a conventional logistic regression model, demonstrating statistically significant performance improvements.