phat 18: e7

Research Article

Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients

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  • @ARTICLE{10.4108/eai.24-8-2021.170752,
        author={Shiva Shankar Reddy and Gadiraju Mahesh and N. Meghana Preethi},
        title={Exploiting Machine Learning Algorithms to  Diagnose Foot Ulcers in Diabetic Patients},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={PHAT},
        year={2021},
        month={8},
        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)},
        doi={10.4108/eai.24-8-2021.170752}
    }
    
  • Shiva Shankar Reddy
    Gadiraju Mahesh
    N. Meghana Preethi
    Year: 2021
    Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients
    PHAT
    EAI
    DOI: 10.4108/eai.24-8-2021.170752
Shiva Shankar Reddy1,*, Gadiraju Mahesh2, N. Meghana Preethi2
  • 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
*Contact email: shiva.shankar591@gmail.com

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.