Application of Data Mining Techniques to Predict Urinary Fistula Surgical Repair Outcome: the Case of Addis Ababa Fistula Hospital, Addis Ababa, Ethiopia.
| dc.contributor.advisor | Jemaneh, Getachew | |
| dc.contributor.advisor | Mola, Mitike(Dr.) | |
| dc.contributor.author | Tefera, Minale | |
| dc.date.accessioned | 2022-06-14T07:05:35Z | |
| dc.date.accessioned | 2023-11-05T15:16:04Z | |
| dc.date.available | 2022-06-14T07:05:35Z | |
| dc.date.available | 2023-11-05T15:16:04Z | |
| dc.date.issued | 2012-06 | |
| dc.description.abstract | Background: The likelihood of the occurrence of incontinence after successful surgical repair makes predicting urinary fistula surgical repair outcome important for decision making during operation and for further follow up and treatment. Objective: The purpose of this thesis is to apply data mining techniques to build a model that can assist in predicting surgical outcome of urinary fistula repair based on clinical assessments done just before surgical repair. Methodology: The six-step hybrid knowledge discovery process model is used as a framework for the overall activities in the study. 15961 instances that have undergone urinary fistula repair in Addis Ababa Fistula Hospital are used for both predictive association rule extraction and predictive model building. Apriori algorithm is used to extract association rules while classification algorithms J48, PART, Naïve Bayes and multinomial logistic regression are used to build predictive models. Support and confidence are used as interestingness measure for association rules while area under the WROC and ROC curve for each specific outcome is sequentially used to compare performances of models from the predictive algorithms. Results: Predictive association rules from Apriori have shown frequent co-occurrence of less severity of injury with cured outcome. The predictive model from PART-M2-C0.05Q1 scheme has shown an area under WROC curve of 0.742. Area under the ROC curve for residual outcome(ROC=0.822) from this algorithm is better than Naïve Bayes and logistic, while the areas under the ROC curves for the other outcomes are greater than the model from J48. Conclusion: Predictive model is developed with the use of PART-M2-C0.05-Q1. It is Residual better in detecting residual outcome than the logistic regression model. The predictiveassociation rules and predictive model built with the use of data mining techniques can assist in predicting urinary fistula surgical repair outcome. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/32006 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Data Mining Techniques ,Fistula Surgical Repair | en_US |
| dc.title | Application of Data Mining Techniques to Predict Urinary Fistula Surgical Repair Outcome: the Case of Addis Ababa Fistula Hospital, Addis Ababa, Ethiopia. | en_US |
| dc.type | Thesis | en_US |