Laboratory Data and IBDQ-Effective Predictors for the Non-Invasive Machine-Learning-Based Prediction of Endoscopic Activity in Ulcerative Colitis

A suitable, non-invasive biomarker for assessing endoscopic disease activity (EDA) in ulcerative colitis (UC) has yet to be identified. Our study aimed to develop a cost-effective and non-invasive machine learning (ML) method that utilizes the cost-free Inflammatory Bowel Disease Questionnaire (IBDQ) score and low-cost biological predictors to estimate EDA. Four random forest (RF) and four multilayer perceptron (MLP) classifiers were proposed. The results show that the inclusion of IBDQ in the list of predictors that were fed to the models improved accuracy and the AUC for both the RF and the MLP algorithms. Moreover, the RF technique performed noticeably better than the MLP method on unseen data (the independent patient cohort). This is the first study to propose the use of IBDQ as a predictor in an ML model to estimate UC EDA. The deployment of this ML model can furnish doctors and patients with valuable insights into EDA, a highly beneficial resource for individuals with UC who need long-term treatment.

Automated summary

This study aimed to develop a cost-effective and non-invasive machine learning (ML) method using the Inflammatory Bowel Disease Questionnaire (IBDQ) score and low-cost biological predictors to estimate endoscopic disease activity (EDA) in ulcerative colitis (UC). The inclusion of IBDQ in the predictors improved accuracy and the AUC for both the random forest (RF) and multilayer perceptron (MLP) classifiers. The RF technique outperformed the MLP method on unseen data, indicating its superior performance. This study is the first to propose the use of IBDQ as a predictor in an ML model to estimate UC EDA.