A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments

Abstract

Chronic kidney disease (CKD) is a gradual loss of kidney function over the period of time and it is irrevocable once functionality reaches the critical state. Detecting the various stages of CKD helps to reduce the progression of the disease. Accurate prediction of CKD stages is one of the urgent needs in the medical industry and it can be effectively done by adopting machine learning (ML) techniques. The primary objective of the present research is to develop an effective classification model for the accurate prediction of CKD stages based on the patient’s health profile as well as the clinical test reports. Here, a hybrid ML strategy is employed that integrates random forest (RF) and AdaBoost (AB) techniques through a voting classifier (VC). The standard CKD dataset with 400 tuples and 25 parameters is used for the proposed investigation. The modification of diet in renal disease (MDRD) equation is used to extract an additional feature known as “estimated Glomerular Filtration Rate (eGFR)” for the prediction of the CKD stage. Pre-processing is carried out on the CKD dataset to fill the missing values by considering the skewness of the parameters and the issue of data leakage is also well addressed. Medically important features are considered and Correlation analysis is carried out to select the appropriate features for the model building process. The proposed Hybrid Ensemble Model (HEM) aids in lowering the bias and variance. HEM model efficiency is assessed using the performance metrics such as cross validation score (CVS), accuracy, precision, recall, F1 measure, Mean Squared Error (MSE), bias and variance and it is compared with the state-of-the-art classification schemes. The outcomes of the analysis reveal that the proposed HEM ensures that the CKD stage prediction is more accurate with 99.16%, 100%, 100% in reduced feature set I, set II, set III and with cross validation score of 97.85%, 99.28%, and 99.64% with reduced features set I, set II and set III respectively.