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The role of Machine Learning in Predicting CABG Surgery Duration
Blekinge Institute of Technology, School of Computing.
Blekinge Institute of Technology, School of Computing.
2011 (English)Independent thesis Advanced level (degree of Master (Two Years))Student thesis
Abstract [en]

Context. Operating room (OR) is one of the most expensive resources of a hospital. Its mismanagement is associated with high costs and revenues. There are various factors which may cause OR mismanagement, one of them is wrong estimation of surgery duration. The surgeons underestimate or overestimate surgery duration which causes underutilization or overutilization of OR and medical staff. Resolving the issue of wrong estimate can result improvement of the overall OR planning. Objectives. In this study we investigate two different techniques of feature selection, compare different regression based modeling techniques for surgery duration prediction. One of these techniques (with lowest mean absolute) is used for building a model. We further propose a framework for implementation of this model in the real world setup. Results. In our case the selected technique (correlation based feature selection with best first search in backward direction) for feature selection could not produce better results than the expert’s opinion based approach for feature selection. Linear regression outperformed on both the data sets. Comparatively the mean absolute error of linear regression on experts’ opinion based data set was the lowest. Conclusions. We have concluded that patterns exist for the relationship of the resultant prediction (surgery duration) and other important features related to patient characteristics. Thus, machine learning tools can be used for predicting surgery duration. We have also concluded that the proposed framework may be used as a decision support tool for facilitation in surgery duration prediction which can improve the planning of ORs and their resources.

Place, publisher, year, edition, pages
2011. , 56 p.
Keyword [en]
Machine learning, surgery duration prediction, operating room planning, data mining
National Category
Computer Science
URN: urn:nbn:se:bth-6074Local ID: diva2:833493
Zahoor Ali 00923339474002 Muhammad Qummer ul Arfeen 0046760652203Available from: 2015-04-22 Created: 2011-10-19 Last updated: 2015-06-30Bibliographically approved

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