A Pilot study on Prediction of Pouchitis in Ulcerative Colitis Patients by Decision Tree Method Versus Logistic Regression Analysis
Background Pouchitis is a non-specific inflammation of the ileal reservoir and the most frequent complication that patients experience in long periods. Diagnosis should be made based on the clinical, endoscopic, and histological aspects. Prediction of pouchitis is an important issue for the physici...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Shiraz University of Medical Sciences
2013
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/bcf2db7debe547ac98f3133b49bf22af |
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| Sumario: | Background Pouchitis is a non-specific inflammation of the ileal reservoir and the most frequent complication that patients experience in long periods. Diagnosis should be made based on the clinical, endoscopic, and histological aspects. Prediction of pouchitis is an important issue for the physician. Objectives The study was aimed to identify the predictive factors of pouchitis as well as their importance. Patients and Methods In the present study, two classification techniques entitled decision trees method and logistic regression analysis were used to help the physician in prediction of pouchitis in ulcerative colitis (UC) patients. These patients are submitted to a specific surgery. The ability of these two methods in prediction is achieved by comparison of the accuracy of the correct predictions (the minimum error rate) and the interpretability and simplification of the results for clinical experts. Results The accuracy rate in prediction is 0.6 for logistic regression method and 0.45 for decision tree algorithm. In addition, the mean squared error is lower for logistic regression (0.41 versus 0.48). However, the area under the ROC is more for decision tree than logistic regression (0.52 and 0.45 respectively). Conclusions The results are not in favor of none of these two methods. However, the simplicity of decision tree for clinical experts and theoretical assumptions of logistic regression method make the choice clear. But more sample size may be needed to choose the best model with more confident. |
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