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327329

Chronic Kidney Disease Classification Using ML Algorithms

Article

Last updated: 23 Dec 2024

OverviewSimilar Items

Subjects

-

Tags

AI & Expert Systems

Abstract

Chronic kidney failure is one of the most common diseases that threaten the lives of many people and cause death for many. By using artificial intelligence, we predict the disease and classify people into infected and non-infected people. One of the goals is to reduce non-communicable disease-related premature death by a third by 2030. 10-15% of the world's population may have chronic kidney disease (CKD), which is one of the major causes of non-communicable disease morbidity and mortality. In order to reduce the effects of patient health complications like hypertension, anaemia (low blood count), mineral bone disorder, poor nutritional health, acid base abnormalities, and neurological complications with timely intervention through appropriate medications, early and accurate detection of the stages of CKD is thought to be essential. Several studies on the early identification of CKD have been conducted utilising machine learning approaches. They weren't primarily concerned with predicting the exact stages. In this work classification methods are used like support vector classifier, random forest, logistic regression, and decision tree. The results detect that Linear SVC Support Vector Machine achieved high accuracy and Random Forest and Decision tree (100%) and logistic regression achieved (96.8%). A data set with 24 feature and 401 records are used for testing the algorithms. 20% of data set will be used in testing and 80% for training. The proposed work achieves high accuracy when compared with the previous works.

DOI

10.21608/kjis.2023.220954.1015

Keywords

Chronic Kidney, Machine Learning, Support Vector Machine, Random Forest

Authors

First Name

Sara

Last Name

shehab

MiddleName

A

Affiliation

Computer Science Dep, Faculty of Computer and Artificial Intelligence, Sadat city, Egypt

Email

sara.shehab@fcai.usc.edu.eg

City

-

Orcid

-

First Name

Eman

Last Name

Shehab

MiddleName

-

Affiliation

Department of computer science, faculty of computers and artificial intellgence, sadat city

Email

eman.shehab@fcai.usc.edu.eg

City

-

Orcid

-

First Name

aya

Last Name

morsi

MiddleName

-

Affiliation

student bioinformatic, faculty of computers and artificial intellgence, sadat city

Email

aya.morsi20@fcai.usc.edu.eg

City

-

Orcid

-

Volume

4

Article Issue

2

Related Issue

44512

Issue Date

2023-11-01

Receive Date

2023-07-04

Publish Date

2023-11-01

Print ISSN

2537-0677

Online ISSN

2535-1478

Article File

KJIS3273291698789600.pdf

PDF . 474.0kB

Link

https://kjis.journals.ekb.eg/article_327329.html

Detail API

https://kjis.journals.ekb.eg/service?article_code=327329

Order

327,329

Type

Original Article

Type Code

462

Publication Type

Journal

Publication Title

Kafrelsheikh Journal of Information Sciences

Publication Link

https://kjis.journals.ekb.eg/

MainTitle

Chronic Kidney Disease Classification Using ML Algorithms

Details

Type

Article

Created At

23 Dec 2024