Comparative analysis of machine learning techniques based on chronic kidney disease dataset

Abstract

Kidney failure is one of the chronic diseases that becoming a very common health issue in the world. It is a state in which kidney is damaged and cannot clean blood as well as they should. Excess fluid and waste may cause more health issue in our body and takes long period to diagnose. Moreover, with no early symptoms, the disease is detected only at a later critical end stage. As this Chronic Disease is becoming threat in today’s world, research is being done on a large scale to predict the presence of this chronic disease using machine learning. Machine learning is playing such a tremendous role in healthcare system like identify diseases and diagnoses, drug discovery and manufacturing, smart health record with the use of various techniques like Support Vector Machine, Decision Tree, Naïve Bayes, Random Forest etc. This paper comparatively analyzes the accuracy of pre-existing techniques for prediction of chronic kidney disease based on data from various research papers. Furthermore, this study also considers the different attributes from either already existing database or from real life database by using multiple techniques of machine learning. It is concluded that working with real life datasets with all possible attributes taken into consideration yields the accurate prediction for the presence of chronic kidney disease using machine learning.