Khalaf, M, Hussain, A, Keight, R, Al-Jumeily, D, Keenan, R, Fergus, P and Idowu, IO (2016) The Utilisiation of composite Machine Learning models for the Classification of Medical Datasets For Sickle Cell Disease. In: 2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC) . (Sixth International Conference on Digital Information Processing and Communications (ICDIPC), 21 April 2016 - 23 April 2016, Lebanon).

Preview

Text PID4179977 - final.pdf - Accepted Version Download (732kB) | Preview

Abstract

The increase growth of health information systems has provided a significant way to deliver great change in medical domains. Up to this date, the majority of medical centres and hospitals continue to use manual approaches for determining the correct medication dosage for sickle cell disease. Such methods depend completely on the experience of medical consultants to determine accurate medication dosages, which can be slow to analyse, time consuming and stressful. The aim of this paper is to provide a robust approach to various applications of machine learning in medical domain problems. The initial case study addressed in this paper considers the classification of medication dosage levels for the treatment of sickle cell disease. This study base on different architectures of machine learning in order to maximise accuracy and performance. The leading motivation for such automated dosage analysis is to enable healthcare organisations to provide accurate therapy recommendations based on previous data. The results obtained from a range of models during our experiments have shown that a composite model, comprising a Neural Network learner, trained using the Levenberg-Marquardt algorithm, combined with a Random Forest learner, produced the best results when compared to other models with an Area under the Curve of 0.995.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	Computer Science & Mathematics
Date Deposited:	05 Apr 2016 12:43
Last Modified:	13 Apr 2022 15:14
URI:	https://researchonline.ljmu.ac.uk/id/eprint/3383

View Item