Title

Performance comparison of classification algorithms for diagnosing chronic kidney disease

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019

Publication Date

11-1-2019

Abstract

Chronic kidney disease (CKD) is one of the diseases with high mortality rate. It is a disease resulted from kidney function loss over a long period of time. The disease shows no symptoms during initial stage. When left not medicated, a person may suffer from other complications such as high blood pressure, anemia, malnutrition, increased risk of cardiovascular disease, cognitive impairment and impaired physical function. Automated diagnosis by using classification algorithms has been an interest of researchers. In this study, six machine learning algorithms were used for classification and its prediction performance was compared based on training time and F1 score, with and without hypertuning the parameters. Of all the six algorithms, KNN has the best F1 score of 0.992248 and minimal training time of 46.999ms. The performance of decision trees was improved with hypertuning, having a F1 score from 0.96 to 0.99. Overall, machine learning algorithms are significant tool to assess chronic kidney disease. © 2019 IEEE.

html

Digitial Object Identifier (DOI)

10.1109/HNICEM48295.2019.9073568

Disciplines

Electrical and Computer Engineering

Keywords

Kidneys—Diseases—Diagnosis—Automation; Perceptrons; Machine learning; Nearest neighbor analysis (Statistics)

Upload File

wf_no

This document is currently not available here.

Share

COinS