Learning Irritable Bowel Syndrome (IBS) Endo-phenotypes from Multidimensional Clinical Data using Machine Learning
Irritable bowel syndrome (IBS) is the most commonly encountered functional gastrointestinal disorder. It has a complex pathophysiology and characterized by a variety of features. It has a worldwide prevalence of approximately 10-14%. Irritable bowel syndrome (IBS) is encountered in the community, primary care, and specialist clinics. IBS has no definitive diagnostic test and doctors are to start with a full medical history and performs tests to exclude other conditions. Due to complex pathophysiology, multiple risk factors, wide range of co-morbidities and phenotypes IBS patients suffers to get a timely and accurate diagnosis. This research worked to identify the most associated comorbidities of IBS through natural data insights and unsupervised machine learning techniques.