A Survey on Classification of Diabetic Foot Ulcer Using Machine Learning Algorithms

Abstract

Diabetes is the most common metabolic disease among the people. This disease may cause problems in blood vessels, nerves and also in the eyes. These problems can cause the foot ulcer of Neuropathic ulcer and Ischemic ulcer. The main reason for this ulcer is the improper circulation of blood and damages in nerves. Because of these ulcers, many people have lost their legs and sometimes it even leads to death. All over the world, millions of people have been affected by this disease. In every thirty seconds, one persons leg is being amputated. So, the proper classification and early detection of foot ulcer is very important for a better treatment. This paper is mainly focused on the classification of foot ulcer with its detailed survey on various classification techniques such as Decision Tree, Random Forest, M5 tree model, Random Tree, REP Tree, Neural Networks, ZeroR, Nae Bayes, Back Propagation Neural Network and Linear Regression model. These algorithms are evaluated using the kaggle dataset. Finally, it shows the comparison of the various classification algorithms.