Diabetes is the main cause for diabetic kidney disease (dkd), which affects the filtering units of kidneys slowly and stops it’s function finally. This consequence is common for both genetic based (type 1) and lifestyle based (type 2) diabetes. However, type 2 diabetes plays a significant influence in increased urine albumin excretion, decreased glomerular filtration rate (gfr), or both. These causes failure of kidneys stage by stage. Herein, the implementation of extended ensemble learning machine algorithm (eelm) with improved elephant herd optimization (ieho) algorithm helps in identifying the severity stages of kidney damage. The data preprocessing and feature extraction process extracts three vital features such as period of diabetes (in year), gfr (glomerular filtration rate), albumin (creatinine ratio) for accurate prediction of kidney damage due to diabetes. Predicted result ensures the better outcome such as an accuracy of 98.869%, 97.899 % of precision ,97.993 % of recall and f-measure of 96.432 % as a result.
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Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India.
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Cite this article
Deepa Devasenapathy, Vidhya K, Anna Alphy, Finney Daniel Shadrach, Kathirvelu M and Jayaraj Velusamy, “Kidney Impairment Prediction Due to Diabetes Using Extended Ensemble Learning Machine Algorithm, Journal of Machine and Computing, vol.3, no.3, pp. 312-325, July 2023. doi: 10.53759/7669/jmc202303027.
