Performance based Evaluation of Algorithmson Chronic Kidney Disease using Hybrid Ensemble Model in Machine Learning
Dhyan Chandra Yadav
and Saurabh PalDepartment of MCA, VBS Purvanchal University, Jaunpur, India.
Corresponding Author E-mail: dc9532105114@gmail.com
Abstract: In medical data science, data classification, pattern generation, data analysis and improving classification accuracy are the important issues in the recent scenario. The main objective of this research to enhanced classification accuracyby four combinations of features technique separately with Neural Network classifier approach.The neural network is analyzed for chronic kidney disease with the help of features reduction and relevanttechniques.In experiment, we used neural network as ensemble model with different features techniques as: Pearson Correlation, Chi-Square, Extra Tree and Lasso regularization. In this research paper, we have prepared training model on 300(75%) instances of chronic kidney disease attributes and testing on 100 (25%) instances.We test the dataset on different applied epochs and calculated accuracy with error rate. The summary of this experiment, we used400 instances with 26 attributes of Chronic Kidney Disease and evaluated highest accuracy calculated (99.98%) with less error rate on passing several epochs by Neural Network ensemble with Lasso model.
Keywords: Correlation Features Selection Method: Extra Tree Chi-Square; Epoch; Error Rate Accuracy; Features Important; Neural Network; Pearson Correlation; Variable Selection and Regularization: Lasso Model Back to TOC