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- W2608875069 abstract "The purpose of this work is to develop classification models that will assist doctors to decide what is the best place to transfer the patient after surgery. This is called post operative decision making process and it is a very important decision making process that could have direct impact on the survival of patients after surgery while also ensuring effective management of the available scarce medical resources. In taking this unique decision, the Doctor looks into several independent variables representing different patient vital signs and their relations in order to discover how they influence the final choice of where to place the patient after surgery. Making an incorrect decision might put the patient life at risk by leading to complications or even death in the worst case. At the same time, the hospital resources are limited for critical places like intensive care unit or even the general medical ward often have limited number of beds. Hence an informed decision need to be taken to minimize post operative complication while also ensuring that available spaces are effectively managed. To assist Doctors in this very critical decision making process we proposed support vector machines and Artificial Neural Network as two classification techniques to ensure correct and effective decision making process. Empirical result indicated that the proposed techniques achieved accuracy of 88.5417% and 82.8125% respectively representing SVM and ANN classifiers." @default.
- W2608875069 created "2017-05-05" @default.
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- W2608875069 date "2017-02-01" @default.
- W2608875069 modified "2023-10-10" @default.
- W2608875069 title "Prediction models aided postoperative decision making based on neural network and support vector machines" @default.
- W2608875069 doi "https://doi.org/10.1109/iciht.2017.7899137" @default.
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