Mahalanobis Outier Removal for Improving the Non-Viable Detection on Human Injuries

Machine learning techniques have been recently applied for discriminating between Viable and Non-Viable tissues in animal wounds, to help surgeons to identify areas that need to be excised in the process of burn debridement. However, the presence of outliers in the training data set can degrade the performance of that discrimination. This paper presents an outlier removal technique based on the Mahalanobis distance to improve the accuracy detection of Non-Viable skin in human injuries. The iteratively application of this technique improves the accuracy results of the Non-Viable skin in a 13.6% when applying K-fold cross-validation.

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