Diagnosis of Chronic Kidney Disease Using Optimised Feature Selection and Ensemble Technique

Olukiran Oyenike Adunni¹ , Shoyemi Olufemi Segun² , Aro Taye Oladele³ , Aiyeniko Olukayode⁴ , Adedokun Olufemi Adewale⁵

Section:Research Paper, Product Type: Journal-Paper
Vol.10 , Issue.3 , pp.19-25, Jun-2022

Online published on Jun 30, 2022

Copyright Β© Olukiran Oyenike Adunni, Shoyemi Olufemi Segun, Aro Taye Oladele, Aiyeniko Olukayode, Adedokun Olufemi Adewale . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Chronic Kidney Disease (CKD) has been identified as an international challenge in healthcare that is increasing progressively. A survey showed that on average more than two million individuals over the world receive dialysis or transplanting kidney treatment to be alive. Prompt diagnosis of CKD is crucial. Prompt and applicable diagnosis demands the use of techniques in data mining. Recently, techniques now extend to a broad area in the diagnosis of a chronic kidney with importance mainly on accuracy via the simplification of disease by employing a selection of features together with pre-processing methods. This paper presented an optimised feature selection approach using the boosting of ensemble technique for CKD diagnostic model by the introduction of a nature-inspired computation algorithm known as Ant Colony Optimization for the selection of attributes from the CKD dataset. Seven selected learning algorithms were used for classification. The CKD diagnostic model was evaluated using an indigenous dataset collected from Ladoke Akintola University of Technology (LAUTECH) teaching hospital, Ogbomoso and Osogbo, University College HospitPredical (UCH), Ibadan, Oyo State and Obafemi Awolowo University Teaching Hospital. Results showed that the optimised CKD diagnostic model produced the best accuracy of 96.54% in Stage 5 of CKD using logistic regression classifier, the best sensitivity of 0.9650 was obtained in Stage 5 of CKD using logistic regression classifier and the best precision of 0.9700 was obtained in Stage 5 of CKD using logistic regression classifier

Key-Words / Index Term :
Chronic Kidney Disease, Diagnosis, Ensemble, Diagnostic Model

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