Abstract
Many studies on diagnosing adult chronic diseases such as diabetes have been achieved by analyzing blood data. Here, we present machine learning algorithm-based diagnostic methods for diabetes by analyzing blood flow oscillations. We used diffuse speckle contrast analysis(DSCA) to measure the blood flow of rats. It can non-invasively measure changes in the relative blood flow of the tissue. Blood flow data acquired from Streptozotocin-induced and control rats were preprocessed by wavelet transform and then classified from machine learning algorithms. In conclusion, the machine learning method can successfully classify two blood flow oscillations in diabetic and control rats.
Published Version
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