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...
| Main Authors: | , , , , , |
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| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
2018
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| Online Access: | 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 |
| _version_ | 1848786520557748224 |
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| author | Ismail, Amelia Ritahani Abdul Aziz, Normaziah Dzaharudin, Fatimah Mat Ralib, Azrina Md Nor, Norzaliza Yahya, Norzariyah |
| author_facet | Ismail, Amelia Ritahani Abdul Aziz, Normaziah Dzaharudin, Fatimah Mat Ralib, Azrina Md Nor, Norzaliza Yahya, Norzariyah |
| author_sort | Ismail, Amelia Ritahani |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T17:10:21Z |
| format | Proceeding Paper |
| id | iium-66159 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:10:21Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-661592021-07-22T01:55:33Z http://irep.iium.edu.my/66159/ Early prediction of acute kidney injury using machine learning algorithms Ismail, Amelia Ritahani Abdul Aziz, Normaziah Dzaharudin, 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 Proceeding Paper 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 Dzaharudin, 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 |
| spellingShingle | QA75 Electronic computers. Computer science Ismail, Amelia Ritahani Abdul Aziz, Normaziah Dzaharudin, Fatimah Mat Ralib, Azrina Md Nor, Norzaliza Yahya, Norzariyah Early prediction of acute kidney injury using machine learning algorithms |
| title | 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_short | Early prediction of acute kidney injury using machine learning algorithms |
| title_sort | early prediction of acute kidney injury using machine learning algorithms |
| topic | QA75 Electronic computers. Computer science |
| 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 |