Early prediction of acute kidney injury using machine learning algorithms

Early prediction of acute kidney injury using machine learning algorithms

The application of machine learning algorithms in the medical sector is gaining increased attention in the last few decades. Thus, the main aim of this manuscript is to compare the performance of well-known machine learning (ML) algorithms to a problem in the domain of medical diagnosis and analyze...

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Main Authors: Ismail, Amelia Ritahani, Abdul Aziz, Normaziah, Dzaharuddin, Fatimah, Mat Ralib, Azrina, Md Nor, Norzaliza, Yahya, Norzariyah
Format: Conference or Workshop Item
Language:English
English
Published: 2018
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://irep.iium.edu.my/66159/
http://irep.iium.edu.my/66159/
http://irep.iium.edu.my/66159/2/Video%20Conference%20APAN%2046%20-%20IIUM.pdf
http://irep.iium.edu.my/66159/1/APAN-Presentation-Final-6Aug2018-1%20%281%29.pdf
id iium-66159
recordtype eprints
spelling iium-661592018-10-01T08:31:44Z http://irep.iium.edu.my/66159/ Early prediction of acute kidney injury using machine learning algorithms Ismail, Amelia Ritahani Abdul Aziz, Normaziah Dzaharuddin, Fatimah Mat Ralib, Azrina Md Nor, Norzaliza Yahya, Norzariyah QA75 Electronic computers. Computer science The application of machine learning algorithms in the medical sector is gaining increased attention in the last few decades. Thus, the main aim of this manuscript is to compare the performance of well-known machine learning (ML) algorithms to a problem in the domain of medical diagnosis and analyze their efficiency in predicting the results. The problem that has been considered in this study is the detection of acute kidney injury (AKI). The ML algorithms are Support Vector Machine (SVM), Neural Network (NN), Deep learning, Decision trees and Naiive Bayes. This research proposed i) an AKI Model: AKI (indicator of renal function) represents a significant risk factor for mortality for patients in ICU, ii) to use analytics to improve clinical decision support by taking advantage of the massive amounts of data and provide right intervention to the right patient at the right time, iii) to use analytics for better care coordination. 2018-08-06 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/66159/2/Video%20Conference%20APAN%2046%20-%20IIUM.pdf application/pdf en http://irep.iium.edu.my/66159/1/APAN-Presentation-Final-6Aug2018-1%20%281%29.pdf Ismail, Amelia Ritahani and Abdul Aziz, Normaziah and Dzaharuddin, Fatimah and Mat Ralib, Azrina and Md Nor, Norzaliza and Yahya, Norzariyah (2018) Early prediction of acute kidney injury using machine learning algorithms. In: Asia Pacific Advanced Network Meeting (APAN 46), 6th August 2018, Auckland, New Zealand. (Unpublished) https://apan.net/meetings/apan46/files/10/10-01-05-01.pdf
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ismail, Amelia Ritahani
Abdul Aziz, Normaziah
Dzaharuddin, Fatimah
Mat Ralib, Azrina
Md Nor, Norzaliza
Yahya, Norzariyah
Early prediction of acute kidney injury using machine learning algorithms
description The application of machine learning algorithms in the medical sector is gaining increased attention in the last few decades. Thus, the main aim of this manuscript is to compare the performance of well-known machine learning (ML) algorithms to a problem in the domain of medical diagnosis and analyze their efficiency in predicting the results. The problem that has been considered in this study is the detection of acute kidney injury (AKI). The ML algorithms are Support Vector Machine (SVM), Neural Network (NN), Deep learning, Decision trees and Naiive Bayes. This research proposed i) an AKI Model: AKI (indicator of renal function) represents a significant risk factor for mortality for patients in ICU, ii) to use analytics to improve clinical decision support by taking advantage of the massive amounts of data and provide right intervention to the right patient at the right time, iii) to use analytics for better care coordination.
format Conference or Workshop Item
author Ismail, Amelia Ritahani
Abdul Aziz, Normaziah
Dzaharuddin, Fatimah
Mat Ralib, Azrina
Md Nor, Norzaliza
Yahya, Norzariyah
author_facet Ismail, Amelia Ritahani
Abdul Aziz, Normaziah
Dzaharuddin, Fatimah
Mat Ralib, Azrina
Md Nor, Norzaliza
Yahya, Norzariyah
author_sort Ismail, Amelia Ritahani
title Early prediction of acute kidney injury using machine learning algorithms
title_short Early prediction of acute kidney injury using machine learning algorithms
title_full Early prediction of acute kidney injury using machine learning algorithms
title_fullStr Early prediction of acute kidney injury using machine learning algorithms
title_full_unstemmed Early prediction of acute kidney injury using machine learning algorithms
title_sort early prediction of acute kidney injury using machine learning algorithms
publishDate 2018
url http://irep.iium.edu.my/66159/
http://irep.iium.edu.my/66159/
http://irep.iium.edu.my/66159/2/Video%20Conference%20APAN%2046%20-%20IIUM.pdf
http://irep.iium.edu.my/66159/1/APAN-Presentation-Final-6Aug2018-1%20%281%29.pdf
first_indexed 2023-09-18T21:33:53Z
last_indexed 2023-09-18T21:33:53Z
_version_ 1777412690980896768

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