Mathematical Model of Two-Dimensional Disease Progression and Categorization of Underlying Vector Fields with Machine Learning

Abstract

A mathematical model was created for simulating disease progression in two-dimension, by specifying a vector field. It was equivalent to observing how a lesion pattern on the skin, could change its shape over time. This model had a parameter that might control the resolution and signal of the pattern. By specifying two different vector fields, we established two sets of subtly different image sets. And, three different supervised machine learning algorithms such as nearest neighbor, decision tree, and support vector machine were used for binary classification. This study was done with Python's numerical (numpy), plotting (matplotlib), and machine learning (sklearn) libraries on the Google Colab platform. All machine learning algorithms were able to distinguish subtle differences produced by different vector fields. Furthermore, the performance of the algorithms were improved by concatenating the beginning and end stages of the pattern and helping the algorithms to pick up the temporal changes. These results demonstrated how AI and machine learning could be adopted in medicine for accurately diagnosing underlying diseases from images.