Diagnosis diabetic foot-based machine learning algorithms

Diabetic foot is a severe medical problem that occurs as a result of high blood sugar levels. It is a common complication in diabetics. Diabetes can lead to complications, especially in the form of diabetic foot problems. If these problems are not detected and treated promptly, they can worsen, leading to severe consequences. Screening methods for the disease can be conventional and do not predict diabetic foot in the early stages. These prompted researchers to find an alternative solution to detect diabetic foot early and non-surgically. Researchers have sought other non-invasive methods to diagnose and predict diabetic feet using image processing techniques and machine learning algorithms. This study presents a comparative performance between six machine learning algorithms (Neural Network, Random Forest, Adaboost, Naïve Bayes) based on a dataset of images of normal and diabetic feet. The results show that Neural Network has an accuracy of 95.6%, the highest performance among other algorithms used in this study.