Prediction of Chronic Venous Insufficiency Condition Using Infrared Thermography and Machine Learning

Chronic venous insufficiency (CVI) is a prevalent medical disorder characterized by the presence of edema, trophic skin changes, and discomfort in the lower extremities resulting from venous hypertension. Loss of mobility caused by CVI can have a devastating impact on both quality of life and productivity. Early and precise diagnosis is critically important for preventing the progression of this disease. Recently, computer-assisted diagnosis (CAD) and artificial intelligence (AI) have become increasingly important in the early detection and treatment of several medical conditions. This study employs a CAD-based classification approach to categorize individuals with normal vascular function and CVI using infrared thermal imaging. Six popular machine learning (ML) classifiers such as Naive Bayes (NB), Multilayer Perceptron (MLP), Random Forest (RF), Logit Boost (LB), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD) have been implemented on the thermal images for the classification of control and CVI subjects. The prediction performance of these ML models is assessed using evaluation measures such as accuracy, sensitivity, specificity, precision, F1 score, and AUC. Compared to the other five ML algorithms that were assessed, the SVM algorithm demonstrated superior accuracy in classifying infrared thermal images into CVI and Non-CVI classes, with a reduced occurrence of misclassification errors.