Detecting ulcerative colitis from colon samples using efficient feature selection and machine learning

• Published in: Scientific reports Vol. 10; no. 1; p. 13744
• Main Authors: Khorasani, Hanieh Marvi, Usefi, Hamid, Peña-Castillo, Lourdes
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.08.2020; Nature Publishing Group
• Subjects: 631/114; 631/114/1305; 631/114/2407; 631/114/2413; 692/699/1503/257; Colitis, Ulcerative - pathology; Colon; Colon - pathology; Diagnosis; Endoscopy - methods; Feature selection; Gene expression; Gene Expression - physiology; Humanities and Social Sciences; Humans; Inflammation - pathology; Inflammatory bowel disease; Inflammatory bowel diseases; Inflammatory Bowel Diseases - pathology; Intestinal Mucosa - pathology; Intestine; Learning algorithms; Machine Learning; Mucosa; multidisciplinary; Science; Science (multidisciplinary); Ulcerative colitis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-70583-0

Ulcerative colitis (UC) is one of the most common forms of inflammatory bowel disease (IBD) characterized by inflammation of the mucosal layer of the colon. Diagnosis of UC is based on clinical symptoms, and then confirmed based on endoscopic, histologic and laboratory findings. Feature selection and machine learning have been previously used for creating models to facilitate the diagnosis of certain diseases. In this work, we used a recently developed feature selection algorithm (DRPT) combined with a support vector machine (SVM) classifier to generate a model to discriminate between healthy subjects and subjects with UC based on the expression values of 32 genes in colon samples. We validated our model with an independent gene expression dataset of colonic samples from subjects in active and inactive periods of UC. Our model perfectly detected all active cases and had an average precision of 0.62 in the inactive cases. Compared with results reported in previous studies and a model generated by a recently published software for biomarker discovery using machine learning (BioDiscML), our final model for detecting UC shows better performance in terms of average precision.