Classification method of peripheral arterial disease in patients with type 2 diabetes mellitus by infrared thermography and machine learning

Peripheral Arterial Disease (PAD) identification is a complex task as a set of different factors cause this disease such as: smoking, diabetes mellitus, old age, hypertension, renal insufficiency, among others. Recently, non-invasive methods based on Infrared Thermography (IRT) are effective for the detection of type-2 diabetes and diabetic foot ulcers from plantar thermograms. However, we have not found studies on the characterization of PAD from the top of the foot. In this work, it is presented a non-invasive methodology for this characterization. We are proposing the analysis of relevant features extracted from IRT images of the upper side of the foot and toes. With these features, we built a Support Vector Classification model that encompasses the data from two groups of Mexican participants one includes twenty-three diabetic patients and the control group has twenty non-diabetic. The average performance of the classification model was estimated under a rigorous bootstrapping method on 1000 randomized and independent runs of 5-fold cross-validations and reached 92.64% of accuracy, 91.80% of sensitivity, and specificity of 93.59%. The experimental data and the source code of the proposed methodology are publicly available; it allows an easy implementation as a supporting tool for physicians in the identification of PAD.