Predicting and Propagation of Diabetic Foot Infection by Deep Learning Model




Metabolic Illness, Neuropathic Ulcer, Blood vessels, Neural Network Models, Foot Ulcer Classification, Deep Learning Algorithms


INTRODUCTION: A deep learning model may be used to predict the occurrence of diabetic foot infections and to understand how these infections spread over time by using sophisticated machine learning methods. Untreated diabetic foot infections, a common diabetic complication, may have devastating effects.

METHODOLOGY: One area where deep learning models—a kind of machine learning—shine is in healthcare, where they are well-suited to deal with data that contains intricate patterns and correlations. The metabolic illness of diabetes affects more individuals than any other.  Neuropathic and Ischemic ulcers are two types of foot ulcers that these issues may cause. Damage to the nerves and blood vessels is the primary cause of this ulcer. Numerous amputations and fatalities have resulted from these sores. There are millions of victims of this illness throughout the globe. The amputation of a human leg occurs once every 30 seconds. The precise anticipation of diabetic foot ulcers has the potential to significantly alleviate the substantial impact of amputation Therefore, it is crucial to correctly categorize foot ulcers and discover them as soon as possible for more effective treatment.

RESULTS: An extensive literature review of classification methods, including decision trees, random forests, the M5 tree method, Random trees, neural network models, ZeroR, Naive Bayes, the Back Propagation Neural Network, Linear Regression model, and Deep Learning Algorithms is presented in this research with a primary emphasis on foot ulcer classification. Using the Kaggle dataset, these algorithms are ranked. In the end, it presents a comparison of different classifiers.


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How to Cite

Kaushal RK, Pagidimalla PRP, Nalini C, Kumar D. Predicting and Propagation of Diabetic Foot Infection by Deep Learning Model. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Apr. 2 [cited 2024 Apr. 21];10. Available from: