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A Framework to Support Ubiquitous Healthcare Monitoring and Diagnostic for Sickle Cell Disease

Khalaf, M, Hussain, A, Al-Jumeily, D, Fergus, P, Keenan, R and Radi, N (2015) A Framework to Support Ubiquitous Healthcare Monitoring and Diagnostic for Sickle Cell Disease. In: Intelligent Computing Theories and Methodologies: Lecture Notes in Computer Science , 9226. (International Conference on Intelligent Computation, 20 August 2015 - 23 April 2016, Fuzhou, China).

Lecture notes in Computer science ICIC Paper 2015.pdf - Accepted Version

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Recent technology advances based on smart devices have improved the medical facilities and become increasingly popular in association with real-time health monitoring and remote/personals health-care. Healthcare organisations are still required to pay more attention for some improvements in terms of cost-effectiveness and maintaining efficiency, and avoid patients to take admission at hospital. Sickle cell disease (SCD) is one of the most challenges chronic obtrusive disease that facing healthcare, affects a large numbers of people from early childhood. Currently, the vast majority of hospitals and healthcare sectors are using manual approach that depends completely on patient input, which can be slowly analysed, time consuming and stressful. This work proposes an alert system that could send instant information to the doctors once detects serious condition from the collected data of the patient. In addition, this work offers a system that can analyse datasets automatically in order to reduce error rate. A machine-learning algorithm was applied to perform the classification process. Two experiments were conducted to classify SCD patients from normal patients using machine learning algorithm in which 99 % classification accuracy was achieved using the Instance-based learning algorithm.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Sickle cell disease Mobile healthcare service Real-time data Self-care management system E-Health
Subjects: Q Science > QA Mathematics > QA76 Computer software
R Medicine > R Medicine (General)
Divisions: Computer Science & Mathematics
Publisher: Springer
Date Deposited: 08 Dec 2016 11:47
Last Modified: 13 Apr 2022 15:14
DOI or ID number: 10.1007/978-3-319-22186-1_66
URI: https://researchonline.ljmu.ac.uk/id/eprint/5049
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