1 Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients Shiva Shankar Reddy 1,* , Gadiraju Mahesh 2 and N. Meghana Preethi 2 1 Research Scholar, Department of Computer Science and Engineering, Biju Patnaik University of Technology, Rourkela, Odisha, India 2 Department of Computer Science and Engineering, Sagi Rama Krishnam Raju Engineering College, Bhimavaram, Andhra Pradesh, India 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. 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). Received on 11 June 2021, accepted on 12 August 2021, published on 24 August 2021 Copyright © 2021 Shiva Shankar Reddy et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited. doi: 10.4108/eai.24-8-2021.170752 1. Introduction Diabetes leads to several complications where foot ulcers are one of them. This disease occurs in type-1 and type-2 diabetic patients. In type-1 diabetic patients, there will be no insulin production or produced in less quantity, so that the patient should depend on insulin injections. In type-2 diabetic patients, insulin will be produced, but it was not sufficient for * Corresponding author. Email: shiva.shankar591@gmail.com the body's functioning. In type-2, the diabetic patient is given medication instead of insulin injections [1]. Long term suffering of either type-1 or type-2 diabetes increases the risk of affecting diabetic foot ulcers. A Diabetic foot ulcer is a condition where the diabetic patient will suffer from a wounded foot. It mostly occurs on the bottom of the foot and stays open. Nearly 15% of diabetic patients will be affected by foot ulcers. Among those people, about 6% are hospitalized because of further complications. On average, EAI Endorsed Transactions on Pervasive Health and Technology Research Article EAI Endorsed Transactions on Pervasive Health and Technology 10 2021 - 11 2021 | Volume 7 | Issue 29 | e2