Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients

Reddy, Shiva Shankar and Mahesh, Gadiraju and Preethi, N. Meghana (2021) Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients. EAI Endorsed Transactions on Pervasive Health and Technology, 7: 29. e2. ISSN 2411-7145

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Abstract

INTRODUCTION: Diabetic foot ulcer (DFU) is a complication of diabetes that affects most of the diabetic patients. It will cause open wounds on the foot. Untreated DFU will lead to amputation and infection, which results in removal of foot or leg. As diabetes is the major health problem faced by people of all age groups, identifying foot ulcers at an early stage is essential. In this context, an efficient model to predict the foot ulcer accurately was proposed in this work.

OBJECTIVES: To predict DFU using an effective neural network algorithm on a suitable dataset that consists of risk factors and clinical outcomes of the disease.

METHODS: In recent days, ML techniques are most commonly used for predicting various diseases. To achieve the objectives a neural network technique, namely extreme learning machine (ELM) is proposed to predict DFU accurately. In addition, three existing algorithms, namely KNN, SVM with Gaussian kernel and ANN are also considered. These are implemented in R programming.

RESULTS: Algorithms compared in terms of five evaluation metrics accuracy, zero-one loss, threat score/critical success index (TS/CSI), false omission rate (FOR) and false discovery rate (FDR). The values of accuracy, 0-1 loss, TS/CSI, FOR and FDR obtained for ELM are 96.15%, 0.0385, 0.95, 0 and 0.05 respectively.

CONCLUSION: After comparison, it was discovered that ELM had outperformed other algorithms in terms of all the metrics. Thus, it was recommended to use ELM over other algorithms while predicting diabetic foot ulcers.

Item Type: Article
Uncontrolled Keywords: Diabetic foot ulcer, KNN, SVM with Gaussian kernel, artificial neural network (ANN), extreme learning machine (ELM), accuracy, zero-one loss, critical success index (CSI), false omission rate (FOR) and false discovery rate (FDR)
Subjects: R Medicine > R Medicine (General)
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 16 Dec 2021 10:35
Last Modified: 16 Dec 2021 10:35
URI: https://eprints.eudl.eu/id/eprint/8965

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