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Prediction of High-Risk Factors in Surgical Operations Using Machine Learning Techniques
Abstract
Prediction of risk during surgical operations is one of the most needed and challenging processes in the healthcare domain. Many researchers use clinical assessment tools to predict perioperative outcomes and postoperative factors in surgical operations. Even though traditional model yields better results, they are not able to achieve promising accuracy due to the enormous growth of data in medical domain. Since the data size grows seamlessly every day, some of the investigators over the past decade use machine learning techniques in their model to predict the risks before and after surgery. Most of the existing systems produced better accuracy by impute missing values in dataset through some common imputation method. However, in order to increase the accuracy level further, two new techniques proposed in this chapter to handle missing values using iterative deepening random forest classifier and identification of surgical risk by using iterative deepening support vector machine. Both of the methods worked well in experimental data set and obtained promising accuracy results.
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