Ensemble Nonlinear Machine Learning Model for Chronic Kidney Diseases Prediction

Living in a major metropolitan area has been linked to an increased risk of developing multiple forms of chronic-kidney-disease(CKD). In developed nations, predicting CKDs is a top priority. Predictive analytics for the purpose of predicting CKDs are the primary focus of this work. However, it is getting harder and harder to forecast outcomes for massive samples. While doing so, the MapReduce architecture makes it possible to write predictive algorithms by combining map and reduce operations. Problems with the scalability and effectiveness of anticipative learning approaches are alleviated by the comparatively straightforward programming interface. To efficiently handle small subsets of massive datasets, the authors propose using an iterative weighted mapreduce approach. Ensemble-nonlinear support-vector-machines(ENSVM) and random-forests(RF) are used to design a binary classification issue. As a result, the suggested approach generates nonlinear blends of kernel activations in example prototypes, as opposed to the conventional linear combination of activations. In addition, an ensemble of deep-SVM is utilized to integrate the descriptors, with the product rule being employed to merge the classifiers' likelihood estimates. Prediction accuracy and results interpretability are used to gauge performance.