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Artificial Neural Learning Based on Big Data Process for eHealth Applications

Artificial Neural Learning Based on Big Data Process for eHealth Applications

Nuno Pombo, Nuno M. Garcia, Kouamana Bousson, Virginie Felizardo
Copyright: © 2016 |Pages: 17
ISBN13: 9781522501596|ISBN10: 1522501592|EISBN13: 9781522501602
DOI: 10.4018/978-1-5225-0159-6.ch067
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MLA

Pombo, Nuno, et al. "Artificial Neural Learning Based on Big Data Process for eHealth Applications." Psychology and Mental Health: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 1524-1540. https://doi.org/10.4018/978-1-5225-0159-6.ch067

APA

Pombo, N., Garcia, N. M., Bousson, K., & Felizardo, V. (2016). Artificial Neural Learning Based on Big Data Process for eHealth Applications. In I. Management Association (Ed.), Psychology and Mental Health: Concepts, Methodologies, Tools, and Applications (pp. 1524-1540). IGI Global. https://doi.org/10.4018/978-1-5225-0159-6.ch067

Chicago

Pombo, Nuno, et al. "Artificial Neural Learning Based on Big Data Process for eHealth Applications." In Psychology and Mental Health: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1524-1540. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0159-6.ch067

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Abstract

The complexity of the clinical context requires systems with the capability to make decisions based on reduced sets of data. Moreover, the adoption of mobile and ubiquitous devices could provide personal health-related information. In line with this, eHealth application faces several challenges so as to provide accurate and reliable data to both healthcare professionals and patients. This chapter focuses on computational learning on the healthcare systems presenting different classification processes to obtain knowledge from data. Finally, a case study based on a radial basis function neural network aiming the estimation of ECG waveform is explained. The presented model revealed its adaptability and suitability to support clinical decision making. However, complementary studies should be addressed to enable the model to predict the upper and lower points related to upward and downward deflections.

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