phat 18: e4

Research Article

Diabetes Correlated Renal Fault Prediction through Deep Learning

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  • @ARTICLE{10.4108/eai.11-11-2020.166958,
        author={Shiva Shankar Reddy and Nilambar Sethi and R. Rajender},
        title={Diabetes Correlated Renal Fault Prediction through Deep Learning},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={PHAT},
        year={2020},
        month={11},
        keywords={Diabetic nephropathy, deep learning technique, naive bayes (NB),CART, logistic regression, support vector machine (SVM), deep belief network(DBN), Machine learning(ML), area under PR curve(AUPR), area under ROC curve (AUROC), gini coefficient and jaccard index},
        doi={10.4108/eai.11-11-2020.166958}
    }
    
  • Shiva Shankar Reddy
    Nilambar Sethi
    R. Rajender
    Year: 2020
    Diabetes Correlated Renal Fault Prediction through Deep Learning
    PHAT
    EAI
    DOI: 10.4108/eai.11-11-2020.166958
Shiva Shankar Reddy1,*, Nilambar Sethi2, R. Rajender3
  • 1: Research Scholar, Department of Computer Science and Engineering, BPUT, Rourkela, Odisha, India
  • 2: Department of Computer Science and Engineering, GIET, Gunupur, Odisha, India
  • 3: Department of Computer Science and Engineering, LENDI Engineering College, Vizianagaram, A.P, India
*Contact email: shiva.shankar591@gmail.com

Abstract

INTRODUCTON: Diabetic nephropathy is one of the complications of diabetes that causes damage to kidneys. Deep learning techniques are widely used to predict different diseases.

OBJECTIVES: The main aim of this work is to develop an effective prediction model using deep learning. To get an effective model, a suitable dataset is considered that comprises of features related to diabetic nephropathy.

METHODS: Deep belief network (DBN) is the proposed deep learning technique which is compared with naive bayes, CART decision tree, logistic regression and support vector machine. DBN is composed of Restricted Boltzmann Machines (RBM). The algorithms are analysed based on evaluation measures like area under PR curve, area under ROC curve, gini coefficient and jaccard index.

RESULTS: After comparison of all algorithms, it was observed that DBN has performed better in terms of AUROC, gini coefficient and jaccard index with values 0.8203, 0.6406 and 0.7777 respectively. But CART obtained better value of 0.9039 only for AUPR.

CONCLUSION: The proposed technique has outperformed other techniques in terms of three metrics and is identified as the best performing algorithm. Hence, it is suggested to use DBN while predicting diabetic nephropathy.