Early prediction of acute kidney injury using machine learning algorithms

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 (Unpublished)

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Abstract

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.

Item Type:	Conference or Workshop Item (Slide Presentation)
Additional Information:	4296/66259
Uncontrolled Keywords:	acute kidney injury, machine learning
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Amelia Ritahani Ismail
Date Deposited:	01 Oct 2018 16:31
Last Modified:	22 Jul 2021 09:55
URI:	http://irep.iium.edu.my/id/eprint/66159

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