Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients

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 pr...

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Autores principales:	Shiva Reddy, Gadiraju Mahesh, N. Preethi
Formato:	article
Lenguaje:	EN
Publicado:	European Alliance for Innovation (EAI) 2021
Materias:	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) Medicine R Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/b889ddddc8d6476db5651187aac3a6b1
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id	oai:doaj.org-article:b889ddddc8d6476db5651187aac3a6b1
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spelling	oai:doaj.org-article:b889ddddc8d6476db5651187aac3a6b12021-11-30T11:07:48ZExploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients2411-714510.4108/eai.24-8-2021.170752https://doaj.org/article/b889ddddc8d6476db5651187aac3a6b12021-11-01T00:00:00Zhttps://eudl.eu/pdf/10.4108/eai.24-8-2021.170752https://doaj.org/toc/2411-7145INTRODUCTION: 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.Shiva ReddyGadiraju MaheshN. PreethiEuropean Alliance for Innovation (EAI)articlediabetic foot ulcerknnsvm with gaussian kernelartificial neural network (ann)extreme learning machine (elm)accuracyzero-one losscritical success index (csi)false omission rate (for) and false discovery rate (fdr)MedicineRMedical technologyR855-855.5ENEAI Endorsed Transactions on Pervasive Health and Technology, Vol 7, Iss 29 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	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) Medicine R Medical technology R855-855.5
spellingShingle	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) Medicine R Medical technology R855-855.5 Shiva Reddy Gadiraju Mahesh N. Preethi Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients
description	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.
format	article
author	Shiva Reddy Gadiraju Mahesh N. Preethi
author_facet	Shiva Reddy Gadiraju Mahesh N. Preethi
author_sort	Shiva Reddy
title	Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients
title_short	Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients
title_full	Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients
title_fullStr	Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients
title_full_unstemmed	Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients
title_sort	exploiting machine learning algorithms to diagnose foot ulcers in diabetic patients
publisher	European Alliance for Innovation (EAI)
publishDate	2021
url	https://doaj.org/article/b889ddddc8d6476db5651187aac3a6b1
work_keys_str_mv	AT shivareddy exploitingmachinelearningalgorithmstodiagnosefootulcersindiabeticpatients AT gadirajumahesh exploitingmachinelearningalgorithmstodiagnosefootulcersindiabeticpatients AT npreethi exploitingmachinelearningalgorithmstodiagnosefootulcersindiabeticpatients
_version_	1718406705456349184

Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients

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