A Comprehensive Analysis on Detecting Chronic Kidney Disease by Employing Machine Learning Algorithms

Nishat, Mirza and Faisal, Fahim and Dip, Rezuanur and Nasrullah, Sarker and Ahsan, Ragib and Shikder, Fahim and Asif, Md. and Hoque, Md. (2021) A Comprehensive Analysis on Detecting Chronic Kidney Disease by Employing Machine Learning Algorithms. EAI Endorsed Transactions on Pervasive Health and Technology, 7: 29. e1. ISSN 2411-7145

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Abstract

INTRODUCTION: Chronic Kidney Disease refers to the slow, progressive deterioration of kidney functions. However, the impairment is irreversible and imperceptible up until the disease reaches one of the later stages, demanding early detection and initiation of treatment in order to ensure a good prognosis and prolonged life. In this aspect, machine learning algorithms have proven to be promising, and points towards the future of disease diagnosis.

OBJECTIVES: We aim to apply different machine learning algorithms for the purpose of assessing and comparing their accuracies and other performance parameters for the detection of chronic kidney disease.

METHODS: The ‘chronic kidney disease dataset’ from the machine learning repository of University of California, Irvine, has been harnessed, and eight supervised machine learning models have been developed by utilizing the python programming language for the detection of the disease.

RESULTS: A comparative analysis is portrayed among eight machine learning models by evaluating different performance parameters like accuracy, precision, sensitivity, F1 score and ROC-AUC. Among the models, Random Forest displayed the highest accuracy of 99.75%.

CONCLUSION: We observed that machine learning algorithms can contribute significantly to the domain of predictive analysis of chronic kidney disease, and can assist in developing a robust computer-aided diagnosis system to aid the healthcare professionals in treating the patients properly and efficiently.

Item Type: Article
Uncontrolled Keywords: Chronic Kidney Disease, Machine Learning Algorithms, UCI Dataset, Accuracy, Precision, Sensitivity, F1 score, ROC
Subjects: 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/8964

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