Gastroesophageal reflux disease is a serious clinical problem, which can significantly impair health-related quality of life, thus having global implications for patients. The first step for a doctor is the clinical classification of the patients, divided into classes after being subjected to endoscopic examinations to control if there are lesions of the esophageal mucosa, and if present, the severity of these lesions. 269 patients were taken into consideration (4 healthy patients, 219 with non erosive reflux disease, 21 with erosive reflux disease, 15 with complicated erosive reflux disease, 10 with Barrett’s disease). A set of values taken from gastroscopy, ph-metry and manometry tests were considered and a decision tree was made to classify every patient. Entropy and information gain were calculated for each node to create the most possible simple tree. The resulting tree presents some paths including a significant number of persons; the values that build these paths can be considered characteristic of each class of patient. This method can be a basis to develop a diagnostic decision support for a training doctor starting from a set of characteristics, specific to a class of patient.

Entropy-Driven Decision Tree Building for Decision Support in Gastroenterology

BERTOLINI, SIMONA;GIACOMINI, MAURO
2013

Abstract

Gastroesophageal reflux disease is a serious clinical problem, which can significantly impair health-related quality of life, thus having global implications for patients. The first step for a doctor is the clinical classification of the patients, divided into classes after being subjected to endoscopic examinations to control if there are lesions of the esophageal mucosa, and if present, the severity of these lesions. 269 patients were taken into consideration (4 healthy patients, 219 with non erosive reflux disease, 21 with erosive reflux disease, 15 with complicated erosive reflux disease, 10 with Barrett’s disease). A set of values taken from gastroscopy, ph-metry and manometry tests were considered and a decision tree was made to classify every patient. Entropy and information gain were calculated for each node to create the most possible simple tree. The resulting tree presents some paths including a significant number of persons; the values that build these paths can be considered characteristic of each class of patient. This method can be a basis to develop a diagnostic decision support for a training doctor starting from a set of characteristics, specific to a class of patient.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/568920
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