DALL·E 2023-12-01 16.17.28 - An intricate image depicting 'Learning Irritable Bowel Syndrome (IBS) Endo-phenotypes from Multidimensional Clinical Data using Machine Learning'. The

Learning Irritable Bowel Syndrome (IBS) Endo-phenotypes from Multidimensional Clinical Data using Machine Learning

Predictive Analytics

Learning Irritable Bowel Syndrome (IBS) Endo-phenotypes from Multidimensional Clinical Data using Machine Learning

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.

Faculty

Students

  • Masabah Bint E Islam (MSDS21, July 2023)

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