Re-investigation of functional gastrointestinal disorders utilizing a machine learning approach
Elahe Mousavi , Ammar Hasanzadeh Keshteli , Mohammadreza Sehhati , Ahmad Vaez ,
Peyman Adibi

Abstract
Background: Functional gastrointestinal disorders (FGIDs), as a group of syndromes with no identified
structural or pathophysiological biomarkers, are currently classified by Rome criteria based on
gastrointestinal symptoms (GI). However, the high overlap among FGIDs in patients makes treatment
and identifying underlying mechanisms challenging. Furthermore, disregarding psychological factors
in the current classification, despite their approved relationship with GI symptoms, underlines the
necessity of more investigation into grouping FGID patients. We aimed to provide more homogenous
and well-separated clusters based on both GI and psychological characteristics for patients with FGIDs
using an unsupervised machine learning algorithm.
Methods: Based on a cross-sectional study, 3765 (79%) patients with at least one FGID were included
in the current study. In the first step, the clustering utilizing a machine learning algorithm was merely
executed based on GI symptoms. In the second step, considering the previous step's results and
focusing on the clusters with a diverse combination of GI symptoms, the clustering was re-conducted
based on both GI symptoms and psychological factors.
Results: The first phase clustering of all participants based on GI symptoms resulted in the formation
of pure and non-pure clusters. Pure clusters exactly illustrated the properties of most pure Rome
syndromes. Re-clustering the members of the non-pure clusters based on GI and psychological
factors (i.e., the second clustering step) resulted in eight new clusters, indicating the dominance of
multiple factors but well-discriminated from other clusters. The results of the second step especially
highlight the impact of psychological factors in grouping FGIDs.
Conclusions: In the current study, the existence of Rome disorders, which were previously defined by
expert opinion-based consensus, was approved, and, eight new clusters with multiple dominant
symptoms based on GI and psychological factors were also introduced. The more homogeneous
clusters of patients could lead to the design of more precise clinical experiments and further targeted
patient care.
Keywords: Cluster analysis; FGID; Functional gastrointestinal disorders; Rome criteria; Unsupervised
machine learning.