Open AccessChronic Kidney Disease Prediction based on Blood Potassium Levels using Machine Learning
Author(s) -
S. Harish,
K. Vinay Kumar,
Krishnil Ram,
G. Pradeepini
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b7113.129219
Subject(s) - kidney disease , clarity , computer science , logistic regression , machine learning , python (programming language) , artificial intelligence , disease , data mining , medicine , pathology , operating system , biochemistry , chemistry
Machine learning is an artificial intelligence(AI) technology that provides the systems with the knowledge and capability to learn and evolve automatically from specifically programmed experiences. This focuses on designing computer programs that are able to gain access and use information on their own. Kidney damage or decreased activity for more than three months is known as chronic kidney disease.This illness occurs when the kidneys can no longer expel extra water or waste from human blood. The goal of this research study is to prepare a predictive modeling for chronic kidney disease data to analyze the different open source python module and output the results predicted by machine learning algorithms and determine the accuracy by comparing different algorithms such as KNN and Logistic Regression which are primarily used for classification of data. This algorithm makes predictions on a dataset collected from the patient's medical records. It gives us the clarity that if someone has chronic kidney disease or not primarily based on a person's blood potassium levels present in their body.