Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Weatherall, D.J.: The role of the inherited disorders of hemoglobin, the first “molecular diseases,” in the future of human genetics. Annu. Rev. Genomics Hum. Genet. 14, 1–24 (2013)
Weatherall, D.J.: The importance of micromapping the gene frequencies for the common inherited disorders of haemoglobin. Br. J. Haematol. 149, 635–637 (2010)
Weatherall, D.J.: The inherited diseases of hemoglobin are an emerging global health burden. Blood 115, 4331–4336 (2010)
Lin, M.K.: Evaluating the acceptance of mobile technology in healthcare: development of a prototype mobile ECG decision support system for monitoring cardiac patients remotely. University of Southern Queensland (2012)
Gillespie, G.: Deploying an IT cure for chronic diseases. Health Data Manage. 8, 68 (2000)
Chan, V., Ray, P., Parameswaran, N.: Mobile e-Health monitoring: an agent-based approach. IET Commun. 2, 223–230 (2008)
Tabish, R., Ghaleb, A.M., Hussein, R., Touati, F., Ben Mnaouer, A., Khriji, L., et al.: A 3G/WiFi-enabled 6LoWPAN-based U-healthcare system for ubiquitous real-time monitoring and data logging. In: 2014 Middle East Conference on Biomedical Engineering (MECBME), pp. 277–280 (2014)
Kim, T.W., Park, K.H., Yi, S.H., Kim, H.C.: A big data framework for u-Healthcare systems utilizing vital signs. In: 2014 International Symposium on Computer, Consumer and Control (IS3C), pp. 494–497 (2014)
Leijdekkers, P., Gay, V.: Personal heart monitoring and rehabilitation system using smart phones. In: International Conference on Mobile Business, ICMB 2006, pp. 29–29 (2006)
Chen, K.-R., Lin, Y.-L., Huang, M.-S.: A mobile biomedical device by novel antenna technology for cloud computing resource toward pervasive healthcare. In: 2011 IEEE 11th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 133–136 (2011)
Yang, S., Jacob, E., Gerla, M.: Web-based mobile e-Diary for youth with sickle cell disease. In: 2012 IEEE Consumer Communications and Networking Conference (CCNC), pp. 385–389 (2012)
Venugopalan, J., Brown, C., Cheng, C., Stokes, T.H., Wang, M.D.: Activity and school attendance monitoring system for adolescents with sickle cell disease. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2456–2459 (2012)
Jung, S.-J., Kwon, T.-H., Chung, W.-Y.: A new approach to design ambient sensor network for real time healthcare monitoring system. In: 2009 IEEE on Sensors, pp. 576–580 (2009)
Doherty, J.A., Reichley, R.M., Noirot, L.A., Resetar, E., Hodge, M.R., Sutter, R.D., et al.: Monitoring pharmacy expert system performance using statistical process control methodology. In: AMIA Annual Symposium Proceedings, p. 205 (2003)
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine learning, 6, 37-66 (1991)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Khalaf, M., Hussain, A.J., Al-Jumeily, D., Fergus, P., Keenan, R., Radi, N. (2015). A Framework to Support Ubiquitous Healthcare Monitoring and Diagnostic for Sickle Cell Disease. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_66
Download citation
DOI: https://doi.org/10.1007/978-3-319-22186-1_66
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22185-4
Online ISBN: 978-3-319-22186-1
eBook Packages: Computer ScienceComputer Science (R0)