Table 3.

The table provides an overview of which type and size of data the models were based on, and the applied techniques in the clinical decision support systems.

Reference No.	Data Presented in the Article	Applied techniques in the clinical decision support systems
[36]	Data consisted of (n=113) images of pressure ulcers on sacrum and hips.	K-means clustering algorithm for image segmentation. Three machine learning approaches (1) Neural Networks, (2) Support Vector Machines, and (3) Random Forest Decision Trees
[37]	Data consisted of (n=73) participants (a mix of soldiers and civilians) with at least one extremity wound >75cm2.	Parametric statistical and machine learning methodologies (1) Bayesian Belief Networks, (2) Random Forest Analysis, and (3) Logistic regression using Least Absolute Shrinkage and Selection Operator. Statistical differences between the continuous variables and wound outcomes were evaluated using the Mann-Whitney U test and the post hoc Tukey-Kramer assessment.
[38]	Data consisted of (n=74) images of chronic wounds from the Medetec medical image database.	Fuzzy divergence-based thresholds used for wound contour segmentation. For wound tissue classification (1) Bayesian classification, and (2) Support vector machine.
[39]	Data consisted of (n=113) images of sacrum and hip pressure ulcers.	Image processing techniques: filtering, kernel smoothing by the mean shift procedure and region growing. Statistical analysis: (1) A hybrid approach based on Neural networks, and (2) Bayesian classifiers.