Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Machine Learning approaches to the application of Disease Modifying Therapy for Sickle Cell using Classification Models

Khalaf, M and Hussain, A and Keight, R and Al-Jumeily, D and Fergus, P and Keenan, R and Tso, P (2017) Machine Learning approaches to the application of Disease Modifying Therapy for Sickle Cell using Classification Models. NEUROCOMPUTING, 228. pp. 154-164. ISSN 0925-2312

[img] Text
Neurocomputing paper for ICIC 2015- Sickle cell disease #final.pdf - Accepted Version
Restricted to Repository staff only until 31 October 2018.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (968kB)

Abstract

This paper discusses the use of machine learning techniques for the classification of medical data, specifically for guiding disease modifying therapies for Sickle Cell. Extensive research has indicated that machine learning approaches generate significant improvements when used for the pre-processing of medical time-series data signals and have assisted in obtaining high accuracy in the classification of medical data. The aim of this paper is to present findings for several classes of learning algorithm for medically related problems. The initial case study addressed in this paper involves classifying the dosage of medication required for the treatment of patients with Sickle Cell Disease. We use different machine learning architectures in order to investigate the accuracy and performance within the case study. The main purpose of applying classification approach is to enable healthcare organisations to provide accurate amount of medication. The results obtained from a range of models during our experiments have shown that of the proposed models, recurrent networks produced inferior results in comparison to conventional feedforward neural networks and the Random Forest model. Results have also indicated that for the recurrent network models tested, the Jordan architecture was found to yield significantly better outcomes over the range of performance measures considered. For our dataset, it was found that the Random Forest Classifier produced the highest levels of performance overall.

Item Type: Article
Uncontrolled Keywords: 08 Information And Computing Sciences, 09 Engineering, 17 Psychology And Cognitive Sciences
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RZ Other systems of medicine
Divisions: Computer Science
Publisher: Elsevier
Date Deposited: 11 Nov 2016 10:14
Last Modified: 07 Sep 2017 13:00
DOI or Identification number: 10.1016/j.neucom.2016.10.043
URI: http://researchonline.ljmu.ac.uk/id/eprint/4141

Actions (login required)

View Item View Item