Kidney Impairment Prediction Due to Diabetes Using Extended Ensemble Learning Machine Algorithm
Deepa Devasenapathy, K Vidhya, Anna Alphy, Finney Daniel Shadrach, Jayaraj Velusamy, M. Kathirvelu
Abstract
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.