Figure 5

Work methodology. The study begins by building the database of the dried blood images taken from 30 healthy volunteers before and after a cycling exercise. These images are then pre-processed individually using different image analysis techniques; cracks filling, Gaussian filter, edge detection, polar coordinate and power spectrum. The database of the logarithmic power spectrum of the images are later used to be passed on to our machine learning algorithm. This algorithm starts with reducing the dimensionality of the image database using principal component analysis. Using the resulting feature space, i.e., the subspace that contains the highest varying principal components, we can move on to the classification step where we use linear discriminant analysis to find the best line that best separates the conditions of the images. The performance of this algorithm is further enhanced by applying an optimising iterative search to select the ‘ideal’ few participants who would best train the algorithm and result in the lowest overall error rate possible.