A new recurrent neural network based predictive model for Faecal   Calprotectin analysis: A retrospective study
  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. 
Zeeshan Khawar Malik, Zain U. Hussain, Ziad Kobti, Charlie W. Lees,   Newton Howard and Amir Hussain
cs.LG